GPU-parallel motion generation for arms — many trajectory optimizations run at once, giving collision-free, smooth plans in milliseconds.面向机械臂的 GPU 并行运动生成：同时运行大量轨迹优化，在毫秒级给出无碰撞且平滑的轨迹。
@misc{sundaralingam2023curobo,
title = {{cuRobo: Parallelized Collision-Free Minimum-Jerk Robot Motion Generation}},
author = {Balakumar Sundaralingam and Siva Kumar Sastry Hari and Adam Fishman and Caelan Garrett and Karl Van Wyk and Valts Blukis and Alexander Millane and Helen Oleynikova and Ankur Handa and Fabio Ramos and Nathan Ratliff and Dieter Fox},
year = {2023},
eprint = {2310.17274},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2310.17274},
}
Tests scaling on humanoid control: more network, data and compute yield a generalist motion-tracking foundation model, with no manual reward engineering.检验规模定律在人形控制上的效果：扩大网络、数据和算力，得到通用动作跟踪基础模型，无需手工设计奖励。
@misc{luo2025sonic,
title = {{SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control}},
author = {Zhengyi Luo and Ye Yuan and Tingwu Wang and Chenran Li and Fernando Castañeda and Sirui Chen and Zi-Ang Cao and Jiefeng Li and David Minor and Qingwei Ben and Jinhyung Park and David Sami and Zi Wang and Xingye Da and Runyu Ding and Cyrus Hogg and Lina Song and Edy Lim and Eugene Jeong and Tairan He and Haoru Xue and Wenli Xiao and Simon Yuen and Jan Kautz and Yan Chang and Umar Iqbal and Linxi "Jim" Fan and Yuke Zhu},
year = {2025},
eprint = {2511.07820},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2511.07820},
}
Showed RL can imitate a wide range of motion clips, flips and spins included, while staying robust. The motion-imitation template much humanoid work builds on.证明强化学习能模仿包括空翻在内的大量动作片段并保持鲁棒，是后续人形动作模仿工作的基本范式。
@misc{peng2018deepmimic,
title = {{DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills}},
author = {Xue Bin Peng and Pieter Abbeel and Sergey Levine and Michiel van de Panne},
year = {2018},
eprint = {1804.02717},
archivePrefix = {arXiv},
primaryClass = {cs.GR},
url = {https://arxiv.org/abs/1804.02717},
}
SCOPE gives diffusion trajectory models calibrated uncertainty tubes without Monte Carlo sampling, by distilling score curvature; tested in crowd navigation and Franka manipulation.SCOPE 通过蒸馏得分函数曲率，让扩散轨迹模型无需蒙特卡洛采样即可给出校准的不确定性管道；在人群导航和 Franka 操作上测试。
@misc{xue2026control,
title = {{Control-Ready Uncertainty for Trajectory Diffusion}},
author = {Zhiwei Xue and Jia Yue Kam and Jinhang Qiu and Yifeng Cheng and Ege Gursoy and Jiaming Wang and Vincent Bonnet and Harold Soh},
year = {2026},
eprint = {2610.12431},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.12431},
}
PyRoki: a modular, cross-platform kinematic optimization toolkit that runs natively on CPU, GPU and TPU.PyRoki：模块化、跨平台的运动学优化工具包，可原生运行在 CPU、GPU 和 TPU 上。
@misc{kim2025pyroki,
title = {{PyRoki: A Modular Toolkit for Robot Kinematic Optimization}},
author = {Chung Min Kim and Brent Yi and Hongsuk Choi and Yi Ma and Ken Goldberg and Angjoo Kanazawa},
year = {2025},
eprint = {2505.03728},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2505.03728},
}
Humanoid controller that scales to ten thousand motion clips with no external stabilising forces and recovers from falls by itself, so resets are not needed.人形控制器可扩展到一万条动作片段且无需外部稳定力，并能自行从跌倒中恢复，无需重置。
@misc{luo2023perpetual,
title = {{Perpetual Humanoid Control for Real-time Simulated Avatars}},
author = {Zhengyi Luo and Jinkun Cao and Alexander Winkler and Kris Kitani and Weipeng Xu},
year = {2023},
eprint = {2305.06456},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2305.06456},
}
Learns reusable skill embeddings for simulated characters by combining adversarial imitation and unsupervised RL, so new tasks need not start from scratch.结合对抗模仿与无监督强化学习，为仿真角色学习可复用的技能嵌入，新任务不必从零训练。
Diffuser — treats planning as denoising whole trajectories, so goals and rewards can steer sampling; an early link between diffusion and control.Diffuser：把规划视为对整条轨迹去噪，目标与奖励可以引导采样；扩散模型与控制结合的早期代表作。
@misc{janner2022planning,
title = {{Planning with Diffusion for Flexible Behavior Synthesis}},
author = {Michael Janner and Yilun Du and Joshua B. Tenenbaum and Sergey Levine},
year = {2022},
eprint = {2205.09991},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2205.09991},
}
Introduces MuJoCo MPC (MJPC), an interactive tool for real-time predictive control in MuJoCo, and shows that a simple sampling-based planner is a strong baseline for contact-rich behaviours.推出 MuJoCo MPC（MJPC），可在 MuJoCo 中交互式地做实时预测控制，并表明简单的基于采样的规划器在接触丰富的行为上是很强的基线。
@misc{howell2022predictive,
title = {{Predictive Sampling: Real-time Behaviour Synthesis with MuJoCo}},
author = {Taylor Howell and Nimrod Gileadi and Saran Tunyasuvunakool and Kevin Zakka and Tom Erez and Yuval Tassa},
year = {2022},
eprint = {2212.00541},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2212.00541},
}
Open-source NMPC + whole-body control + state-estimation stack for quadrupeds built on OCS2 and ros-control, with a sim-to-real path; a popular starting point for model-based legged control.基于 OCS2 与 ros-control 的开源四足控制栈：非线性 MPC + 全身控制 + 状态估计，并支持 sim2real；基于模型的足式控制常用的起点。
Ruckig — open-source C++/Python library for time-optimal, jerk-limited online trajectories to any target position, velocity and acceleration; a common drop-in for smooth arm motion on real controllers.Ruckig：开源 C++/Python 库，可实时生成到任意目标位置、速度和加速度的时间最优、限加加速度（jerk）轨迹；常被直接用于真实控制器上的机械臂平滑运动。
C++ toolbox for nonlinear MPC on switched systems (SLQ, DDP, SQP) with robot examples for legged robots and mobile manipulators; the base of many open-source legged MPC + whole-body control stacks.面向切换系统的非线性 MPC C++ 工具箱（SLQ、DDP、SQP），附足式机器人与移动操作臂示例；许多开源足式 MPC + 全身控制方案都基于它。
A fast, robust ADMM-based QP solver with warm starting and embedded C code generation; widely used inside MPC and whole-body controllers.基于 ADMM 的快速、鲁棒二次规划求解器，支持热启动与嵌入式 C 代码生成，广泛用于 MPC 与全身控制器。
@misc{stellato2017osqp,
title = {{OSQP: An Operator Splitting Solver for Quadratic Programs}},
author = {Bartolomeo Stellato and Goran Banjac and Paul Goulart and Alberto Bemporad and Stephen Boyd},
year = {2017},
eprint = {1711.08013},
archivePrefix = {arXiv},
primaryClass = {math.OC},
url = {https://arxiv.org/abs/1711.08013},
}
An open-source DDP-based solver for contact-rich trajectory optimization, widely used for legged and humanoid motion generation and MPC.基于 DDP 的开源接触丰富轨迹优化求解器，广泛用于足式与人形机器人的运动生成和 MPC。
@misc{mastalli2019crocoddyl,
title = {{Crocoddyl: An Efficient and Versatile Framework for Multi-Contact Optimal Control}},
author = {Carlos Mastalli and Rohan Budhiraja and Wolfgang Merkt and Guilhem Saurel and Bilal Hammoud and Maximilien Naveau and Justin Carpentier and Ludovic Righetti and Sethu Vijayakumar and Nicolas Mansard},
year = {2019},
eprint = {1909.04947},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/1909.04947},
}
Fast nonlinear MPC and estimation solvers with Python/MATLAB interfaces that generate C code for embedded targets.高速非线性 MPC 与状态估计求解器，提供 Python/MATLAB 接口并可为嵌入式平台生成 C 代码。
@misc{verschueren2019acados,
title = {{acados: a modular open-source framework for fast embedded optimal control}},
author = {Robin Verschueren and Gianluca Frison and Dimitris Kouzoupis and Jonathan Frey and Niels van Duijkeren and Andrea Zanelli and Branimir Novoselnik and Thivaharan Albin and Rien Quirynen and Moritz Diehl},
year = {2019},
eprint = {1910.13753},
archivePrefix = {arXiv},
primaryClass = {math.OC},
url = {https://arxiv.org/abs/1910.13753},
}
Fast C++/Python rigid-body dynamics library with analytical derivatives; a common backbone for kinematics, optimal control (e.g. Crocoddyl) and legged/humanoid control stacks.高性能 C++/Python 刚体动力学库，提供解析导数；是运动学、最优控制（如 Crocoddyl）以及足式/人形机器人控制栈的常用底座。
Convex MPC for quadrupeds — simplified rigid-body dynamics turn ground-reaction-force planning into a small QP solved in real time; the classic model-based baseline behind many open-source quadruped controllers.四足机器人的凸 MPC：用简化刚体动力学把足端地面反作用力规划变成可实时求解的小规模 QP；众多开源四足控制器背后的经典基于模型的基线。
Model-based toolbox for robotics - multibody dynamics with contact simulation, mathematical programming and trajectory optimization; used in the MIT manipulation and underactuated courses.面向机器人的基于模型的工具箱：带接触的多体动力学仿真、数学规划与轨迹优化；MIT 操作与欠驱动课程即使用它。
The standard library of sampling-based motion planners (RRT, RRT-Connect, PRM, RRT* and many more); the default planning backend of MoveIt.采样式运动规划器的标准库（RRT、RRT-Connect、PRM、RRT* 等众多算法），也是 MoveIt 默认的规划后端。
@misc{huang2026cuarms,
title = {{cuArms: GPU-Accelerated Conflict-Based Planning for Multi-Arm Manipulation}},
author = {Philip Huang and Fabio Ramos and Jiaoyang Li and Caelan Garrett and Lars Johannsmeier},
year = {2026},
eprint = {2610.36250},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.36250},
}
CORD-SLS pairs a GPU-parallel differentiable simulator with robust MPC and conformal bounds for rope and cloth, reporting millisecond planning with safety margins on hardware.CORD-SLS 将 GPU 并行可微仿真与鲁棒 MPC、共形预测误差界结合，用于绳索和布料操作，报告在实机上以毫秒级规划并保持安全裕度。
@misc{li2026robustness,
title = {{Robustness without Wrinkles: Parallel Simulation and Robust MPC for Certified Deformable Manipulation}},
author = {Wei-Chen Li and Jeffrey Fang and Sasanka Polisetti and Yuexi Song and Glen Chou},
year = {2026},
eprint = {2606.14188},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.14188},
}
FLoRe: robot-free demos from force-sensing UMI-style grippers + body trackers train a diffusion policy that outputs per-hand force commands for a compliant whole-body controller; reports 80% vs. 26.7% for position-only.FLoRe：用带力传感的 UMI 式手持夹爪与人体追踪器采集无机器人示教，训练扩散策略输出每只手的力指令，由柔顺全身控制器执行；报告在三个接触密集任务上成功率 80%，仅位置控制为 26.7%。
@misc{li2026learning,
title = {{Learning Force-Reactive Humanoid Loco-Manipulation}},
author = {Sihang Li and Xiaofeng Guo and Jinkun Cao and Charlie Cheng and Ruihan Yang and Chen Feng and Guanya Shi and Haozhi Qi},
year = {2026},
eprint = {2610.35862},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.35862},
}
Smooth analytic contact gradients only in the backward pass, hard-contact forward rollout unchanged: a quadruped trotting policy trains in under 2 min on a desktop and transfers to hardware without fine-tuning.只在反向传播中换用平滑的解析摩擦接触梯度，前向仍是硬接触仿真：四足小跑策略在台式机上不到 2 分钟训练完成，无需微调即可迁移到真机；在接触隐式轨迹优化中也能找到更丰富的接触解。
@misc{kim2026diffcontact,
title = {{DiffContact: Differentiable Contact Dynamics for Policy Learning and Contact-Implicit Trajectory Optimization}},
author = {Gijeong Kim and Seungbin Oh and Dongyun Kang and Hae-Won Park},
year = {2026},
eprint = {2610.35852},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.35852},
}
@misc{yi2026swap,
title = {{SWAP: Test-Time Steerable Humanoid Whole-Body Collision Avoidance}},
author = {Zeji Yi and Guanqi He and Xiaofeng Guo and Guannan Qu and Pieter Abbeel and Yan Duan and Karen Liu and Guanya Shi},
year = {2026},
eprint = {2610.25711},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.25711},
}
SMR distils physics experts that track robot-aware targets into a strictly causal Transformer, retargeting human motion to humanoids frame by frame. Reports better closed-loop results than kinematic baselines.SMR 先让物理专家在仿真中跟踪由人体动作转换来的机器人任务空间目标，再用其执行轨迹训练严格因果的 Transformer，实现逐帧流式的人形动作重定向；报告在相同全身控制器下闭环表现优于运动学与物理精修基线。
@misc{gu2026smr,
title = {{SMR: Learning streaming motion retargeting for humanoids from human semantic}},
author = {Chun Gu and Juntong Zhang and Jingyu Li and Li Zhang},
year = {2026},
eprint = {2610.29169},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.29169},
}
One frozen humanoid policy driven by prompts (timed body-part targets, velocity, style); skills are plain code, refined by coding agents. On a Unitree G1 it walks, recovers, picks up objects and sits.可“提示”的人形全身执行层：冻结的生成式基础策略接收部分目标（定时的身体部位目标、速度指令、风格）生成全身动作；技能是普通代码，可由编码智能体分析动作数据后自动改进，无需按任务重训。在 Unitree G1 上一个检查点即可行走、恢复、遥操作抓取和坐下。
@misc{tessler2026metatron,
title = {{METATRON: A Promptable Whole-Body Execution Layer for Humanoids}},
author = {Chen Tessler and Yifeng Jiang and Alec Otto and Yi Shi and Umar Iqbal and Gal Chechik and Xue Bin Peng},
year = {2026},
eprint = {2610.38027},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.38027},
}
Picks at runtime which low-level policy to run and with which goal, using a learned energy per policy. New policies join by training their own energy model, with no retraining of others. Deployed on a real humanoid.运行时为每个底层策略学习能量函数，据此选择执行哪个策略、给它什么目标。新增策略只需训练自己的能量模型，无需重训其他策略或中央调度器；已部署到真实人形的移动操作任务。
@misc{ye2026energy,
title = {{Energy-Based Policy Orchestration}},
author = {Weirui Ye and Yen-Jen Wang and Elvis Hsieh and Zhiyang Dou and Shibo Zhao and Pieter Abbeel and Yilun Du and Leslie Pack Kaelbling and Tomás Lozano-Pérez},
year = {2026},
eprint = {2611.02546},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.02546},
}
@misc{qi2026humanoid,
title = {{Humanoid-GPT: Scaling Data and Structure for Zero-Shot Motion Tracking}},
author = {Zekun Qi and Xuchuan Chen and Dairu Liu and Chenghuai Lin and Yunrui Lian and Sikai Liang and Zhikai Zhang and Yu Guan and Jilong Wang and Wenyao Zhang and Xinqiang Yu and He Wang and Li Yi},
year = {2026},
eprint = {2606.03985},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.03985},
}
Separates performance from safety on humanoids: the nominal policy handles the task while a residual RL policy acts as an implicit safety filter, avoiding competing reward terms.在人形机器人上分离性能与安全：名义策略负责任务，残差 RL 策略充当隐式安全过滤器，避免相互竞争的奖励项。
@misc{qu2026ressafe,
title = {{ResSafe: Learning Safety Filtering with Residual Reinforcement Learning for Humanoids}},
author = {Gechen Qu and Tong Zhang and Bike Zhang and Yen-Jen Wang and Koushil Sreenath and Claire Tomlin and Jason Jangho Choi},
year = {2026},
eprint = {2609.15988},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2609.15988},
}
Adds an explicit 3-D force command per contact body to motion tracking, set online by a planner. On hardware it pushes and kicks boxes up to 20 kg where a force-blind baseline fails, and adapts to added loads.在动作跟踪中为每个接触部位加入显式三维力指令，由高层规划器在线给出。实机可推、踢最重 20 kg 的箱子（不感知力的基线会失败），并能适应任务中途增加的负载。
@misc{zhao2026ctrl,
title = {{Ctrl-F: Force as an Interface for Humanoid Loco-Manipulation}},
author = {Mengjie Zhao and Yuxin Chen and Yiyang Shao and Chuye Hong and Jiakang Chen and Koushil Sreenath and Masayoshi Tomizuka},
year = {2026},
eprint = {2611.05507},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.05507},
}
Splits humanoid whole-body control: inverse-dynamics feedforward plus PD give tracking torques, an RL residual adds only balance corrections; one balance policy carries across motion datasets, payloads and contact.拆分人形全身控制：逆动力学前馈加 PD 反馈提供跟踪力矩，强化学习残差只负责平衡修正；同一平衡策略可跨动作数据集、负载与物理交互泛化，仿真与真机均有验证。
@misc{wei2026dkwbc,
title = {{DKWBC: Decoupling Motion Tracking and Balancing for Humanoid Whole-Body Control}},
author = {Dingkun Wei and Zehong Shen and Huaijin Pi and Xueyang Wang and Xing Zhu and Hujun Bao and Yujun Shen and Xiaowei Zhou},
year = {2026},
eprint = {2611.09605},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.09605},
}
An open-source ROS 2 multimode controller that switches control modes and triggers safety reflexes in under a millisecond, without stopping the task; shown on a Franka arm.开源的 ROS 2 多模式控制器，可在亚毫秒内切换控制模式并触发安全反射而无需中断任务；在 Franka 机械臂上验证。
Builds batched GPU collision checking and forward kinematics once and runs sampling, search and optimization planners on top, so the three paradigms can be compared fairly.一次性实现批量 GPU 碰撞检测与正运动学，在其上运行采样、搜索和优化三类规划器，使三种范式可以公平比较。
@misc{wu2026prismplan,
title = {{PrismPlan: A Shared GPU Substrate for Motion Planning Across Paradigms}},
author = {Hong Wu and ChaoXuan Liu and ShuJie Wang},
year = {2026},
eprint = {2610.23884},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.23884},
}
Evaluates batches of kinematics, Jacobians and Hessians on the GPU with low wall-clock latency, targeting online sampling- and gradient-based trajectory optimization.在 GPU 上以低延迟批量计算运动学、雅可比与海森矩阵，面向在线运行的采样式与梯度式轨迹优化。
@misc{viljoen2026cusadi,
title = {{CusADi2: Latency-Optimized Batch Computation of Robot Kinematics, Jacobians, and Hessians on the GPU}},
author = {John Viljoen and Lasse Peters and Masayoshi Tomizuka and Negar Mehr},
year = {2026},
eprint = {2610.25476},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.25476},
}
Signed friction distance as a task-adaptive slip margin for bimanual tilting about a pivot; a sequence of SOCPs gives optimal contact forces. Real tests show adaptive margins beat fixed or no margins.以带符号摩擦距离度量防滑裕度，并在双臂绕支点倾转物体时按任务需求自适应调整；一系列二阶锥规划给出全局最优接触力。真机实验显示自适应裕度优于固定裕度和无裕度。
@misc{lee2026keeping,
title = {{Keeping Contact: Task-Adaptive Friction Margins for Bimanual Non-Prehensile Manipulation}},
author = {Qian Ying Lee and Lin Yang and Domenico Campolo},
year = {2026},
eprint = {2610.36961},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.36961},
}
@misc{wang2026generalist,
title = {{A generalist policy for muscle-driven control across individual musculoskeletal models}},
author = {Bozhao Wang and Yinglei Zhu and Zhengqing Li and Zhiyuan Zhang and Weiming Kang and Yiheng He and XinJun Liu and Senhao Hou and Huichan Zhao and Xiaoqiang Tang},
year = {2026},
eprint = {2610.36535},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.36535},
}
CoRL 2026 Spotlight. A module for contact-rich motion transfer across sim-to-real gaps, payloads and robot instances, as an alternative to domain randomization or system ID.CoRL 2026 Spotlight。面向富接触动作迁移的模块，应对仿真到现实、负载与个体差异，用于替代域随机化或系统辨识。
@misc{son2026tam,
title = {{TAM: Torque Adaptation Module for Robust Motion Transfer in Manipulation}},
author = {Dongwon Son and Florian Shkurti and Jason Lee and Naman Shah and Beomjoon Kim and Dieter Fox},
year = {2026},
eprint = {2606.06218},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.06218},
}
CoRL 2026 Spotlight. A morphology-conditioned implicit neural model of pose reachability, differentiable, self-collision aware and able to generalize to unseen morphologies.CoRL 2026 Spotlight。以形态为条件的隐式神经位姿可达性模型，可微、考虑自碰撞，并能泛化到未见形态。
@misc{walter2026ram,
title = {{RAM: Reachability Across Morphologies}},
author = {Tim Walter and Xinyu Chen and Jonathan Külz and Matthias Althoff},
year = {2026},
eprint = {2606.09108},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.09108},
}
Learned goal-conditioned inverse kinematics that can hold the offset a position controller needs at rest; reports errors within 1 mm and 0.2° on 93.5% of held-out simulated tasks and training directly on a real arm.学习式目标条件逆运动学，能保持位置控制器静止时所需的偏置；报告在 93.5% 的留出仿真任务上误差低于 1 mm 和 0.2°，并可直接在真实机械臂上训练。
@misc{liu2026policyik,
title = {{PolicyIK: High-Precision Inverse Kinematics for Robot Arms with Reinforcement Learning}},
author = {Yuhan Liu and Xinqi Liu and Zixuan Dong and Keith W. Ross and Shenji Wan},
year = {2026},
eprint = {2611.05372},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.05372},
}
Makes deep unfolding work for large conic solvers via matrix-free implicit differentiation and a stable PSD-projection gradient; reports up to 50× speedups on SDP-based planning.通过无矩阵隐式微分和稳定的半正定投影梯度，让深度展开适用于大规模锥优化求解器；报告在基于 SDP 的规划上最高提速 50 倍。
Kinesthetic teaching for real-world RL that keeps human guidance within the policy's kinematic limits, plus an impedance control stack. Reports 23–48% shorter cycle time vs HIL-SERL on industrial insertion.面向真实世界强化学习的示教框架：人手拖动示教时轨迹始终满足策略可执行的运动学限制，并配套阻抗控制栈。报告工业插装任务节拍时间比 HIL-SERL 缩短 23–48%。
Flow-matching planner whose sampling steps follow the execution dynamics, so safety (high-order CBFs) is enforced only on the executed step. Lower latency and better safety on navigation, racing and locomotion.流匹配规划器让采样过程与系统执行动力学对齐，只需对实际执行的一步施加高阶控制障碍函数约束。在导航、竞速和运动任务上降低规划延迟并提升安全性。
@misc{jang2026safe,
title = {{Safe Streaming Flow Planning by Aligning Sampling Dynamics with Execution Dynamics}},
author = {Seunghwan Jang and Jeongyong Yang and Siddharth Ancha and SooJean Han},
year = {2026},
eprint = {2610.03132},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.03132},
}
Uses constrained IK, where solution manifolds are known exactly, to test what geometry diffusion models learn. On UR5 and Franka the intrinsic dimension read from the score matches the analytic degrees of freedom.以解流形可精确知道的约束逆运动学为试验场，检验扩散模型学到的几何结构。在 UR5 和 Franka 上，从得分函数估计的内在维度与解析自由度一致。
LLM-driven evolutionary search over mode-switching controllers written as programs, so cheap modes run in easy task phases. Reports designs beyond the Pareto front of one-size-fits-all controllers.用 LLM 驱动的进化搜索，把多模式切换控制器写成可解释程序并迭代优化，使简单阶段使用低开销模式。报告找到的设计超越单一结构控制器的帕累托前沿。
Replaces a policy MLP's first layer with learned multiplicative (bilinear) features, plug-and-play for RL and imitation. Reports humanoid survival up from 51.0% to 92.5%, holding under friction and payload shifts.把策略 MLP 的第一层替换为可学习的乘性（双线性）特征，即插即用于强化学习与模仿学习。报告人形机器人存活率从 51.0% 提升到 92.5%，在摩擦、负载变化下依然有效。
Splits force regulation into a direction part learned in sim (task frame and control-mode outputs of a VLA-based policy) and a magnitude part set by hand in an admittance controller. Sim data only; four real tasks.把力调节拆成两部分：方向分量（任务坐标系与控制模式，由基于 VLA 的策略在仿真数据上学习）和幅值分量（在导纳控制器中人工设定）。只用仿真数据，在四个真机任务上验证。
@misc{yang2026tdc,
title = {{TDC: Sim-to-Real Transferable Directional Compliance for Contact-Rich Manipulation}},
author = {Yifei Yang and Anzhe Chen and Zhenjie Zhu and Kechun Xu and Yunxuan Mao and Yufei Wei and Lu Chen and Rong Xiong and Yue Wang},
year = {2026},
eprint = {2602.14174},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2602.14174},
}
Fine-tunes an LLM with solver-based RL rewards to write executable mixed-integer programs for multi-modal motion planning (e.g. walk, then grasp), instead of answering directly.用求解器结果作为奖励对 LLM 做强化学习微调，让它为多模态运动规划（如先走再抓）编写可执行的混合整数规划代码，而不是直接给出答案。
@misc{sun2026m,
title = {{M$^3$P-R1: Reinforcement Learning for Large Language Model Guided Multi-Modal Motion Planning via MIP Code Generation}},
author = {Xingpeng Sun and Zherong Pan and Kai Cheng and Xindi Tang and Syed Talha Bukhari and Aniket Bera},
year = {2026},
eprint = {2609.18669},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2609.18669},
}
Learns neural CBFs from an HJ value approximation; with control-affine dynamics and polytope inputs the optimum is at a vertex, so GPU vertex tree search makes labels. 15 systems + a robot among pedestrians.通过近似 HJ 价值函数学习神经控制障碍函数；在控制仿射动力学和多面体输入集下最优解位于顶点，于是用 GPU 并行顶点树搜索生成监督点。在 15 个系统和行人避让真机实验中验证。
@misc{derajic2026vertexcbf,
title = {{VertexCBF: Improving Neural Control Barrier Functions via Vertex-Restricted Control Search}},
author = {Bojan Derajić and Sebastian Bernhard and Wolfgang Hönig},
year = {2026},
eprint = {2609.12831},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2609.12831},
}
A causal transformer predicts a rollout (in place of a model) and a residual fix to a nominal controller; a barrier critic scores CBF violations along the rollout, so safety looks ahead like MPC.因果 Transformer 预测未来推演（代替动力学模型）并给名义控制器输出残差修正；障碍评估器沿推演检查 CBF 约束违例，从而像 MPC 一样前瞻安全，却无需在线求解优化。
Learns an invertible map plus convex polytopes in its latent space, so convex-set planners work in high-DoF spaces and paths decode back feasible. Tested from 2D to 14-DoF; no false positives after refinement.联合学习可逆映射及其潜空间中的凸多面体并集，使基于凸集的规划可用于高自由度空间，解码回原空间的路径仍可行。在 2D 至 14 自由度场景测试，细化后未观察到误报。
@misc{yoon2026learning,
title = {{Learning Unions of Convex Sets via Invertible Latent Decomposition for Path Planning}},
author = {Taerim Yoon and Dongho Kang and Kisang Park and Junha Cha and Stelian Coros and Sungjoon Choi},
year = {2026},
eprint = {2606.12027},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.12027},
}
With a limited budget for re-estimation and replanning under drifting dynamics, a dynamic-regret bound gives an online rule for when to update. Tested on simulated rover slip and a Crazyflie quadrotor.在动力学漂移、重估与重规划次数有限时，用动态遗憾界推导出何时更新的在线规则。在模拟的打滑漫游车和 Crazyflie 四旋翼上验证。
@misc{musavi2026when,
title = {{When Should a Robot Replan? Regret-Guided Update Scheduling in Time-Varying MDPs}},
author = {Negin Musavi and Gokul Puthumanaillam and Ruben Hernandez and William Schafer and Melkior Ornik},
year = {2026},
eprint = {2606.16972},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.16972},
}
Theory: gives conditions under which sampled reachable sets are accurate in Hausdorff distance, and shows the sample need grows exponentially in both state dimension and horizon, with a matching lower bound.理论工作：给出采样可达集在 Hausdorff 距离意义下准确的条件，并证明所需样本数随状态维度和时间跨度都呈指数增长，且给出相应下界。
@misc{liu2026limits,
title = {{On the Limits of Sampling-Based Reachability: Geometry, Dynamics, and Sample Complexity}},
author = {Jixian Liu and Ihab Tabbara and Hussein Sibai and Enrique Mallada},
year = {2026},
eprint = {2607.18606},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2607.18606},
}
Adds a residual dynamics network and a learned Q critic to iLQR MPC, with a GPU batched iLQR that solves thousands of problems in parallel, for model-based RL when contacts are poorly modeled.在 iLQR 模型预测控制中加入残差动力学网络和学习得到的动作价值评估器，并实现 GPU 批量 iLQR 并行求解数千个问题，用于接触难以建模时的基于模型强化学习。
@misc{schoch2026optimal,
title = {{Optimal Control with Learned Critics under Unmodeled State Dependencies}},
author = {Philipp Schoch and Markus Ryll},
year = {2026},
eprint = {2610.05359},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.05359},
}
Recasts divergent components of motion as unstable Koopman eigenfunctions learned from about an hour of real biped data; they improve walking tracking and give viability constraints for MPC.把发散运动分量（DCM）重新表述为不稳定的 Koopman 特征函数，仅用约一小时真实双足数据学习得到；可改善行走跟踪，并为 MPC 提供可行性约束。
λ-Reachability learns Hamilton-Jacobi safety values with a TD(λ)-like multi-step estimator with a random horizon, proven to be a contraction; better safe-set estimates on simulated and real humanoids.λ-Reachability 用类似 TD(λ)、随机视野的多步估计器学习 Hamilton-Jacobi 安全值函数，并证明其为压缩映射；在仿真与真实人形上安全集估计更准确。
@misc{chen2026reachability,
title = {{$λ$-Reachability: Geometric-Horizon Safety Bellman Equations for Humanoid Safety}},
author = {Rui Chen and Shangtao Li and Yifan Sun and Changliu Liu},
year = {2026},
eprint = {2606.16022},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.16022},
}
MPC-Injection steers off-policy RL toward a usable gait by inserting MPC-generated transitions into training, with no reward shaping or discriminator; Go2 sim-to-real, and a barrel roll pure RL misses.MPC-Injection 把 MPC 生成的状态转移注入离策略强化学习训练，在不改奖励、不加判别器的情况下把策略引向可用步态；在 Go2 上实现仿真到真实，并完成纯 RL 学不会的侧滚翻。
VIGOR-MPC distills a privileged long-horizon MPC into a short-horizon MPC whose cost weights are inferred online from noisy vision; in vision-based racing it nears the teacher's lap times with 90%+ less compute per step.VIGOR-MPC 将具有特权信息的长视野 MPC 蒸馏为短视野 MPC，其代价参数由含噪视觉在线推断；在视觉自主竞速中接近教师圈速，每步计算量减少 90% 以上。
Shows predictive sampling, MPPI, CEM and their variants are one SMC algorithm set by proposal, update rule and resampling threshold. Resampling cuts long-horizon collisions by 30%; programmable proposals lift push-T.指出预测采样、MPPI、CEM 及其变体都是同一个序贯蒙特卡洛算法，只是提议分布、更新规则与重采样阈值不同。重采样使长时域碰撞率降低 30%，可编程提议显著提升 push-T 成功率。
@misc{garrett2026sampling,
title = {{Sampling-based Model Predictive Control as Sequential Monte Carlo Inference}},
author = {Austin Garrett and Muyang Yan and Zachary Kingston},
year = {2026},
eprint = {2611.10877},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.10877},
}
Shows termination-based constrained RL can learn to avoid the goal when constraints tighten near it, and fixes this with a dense success critic; validated on spacecraft docking tasks.发现基于终止的约束强化学习在目标附近约束收紧时会学会回避目标，并用稠密成功信号的辅助价值网络解决；在航天器对接任务上验证。
One policy learns full-body interaction with varied objects from hours of imperfect MoCap: subject-specific teachers refine the data first, then scale up.单一策略从数小时不完美动捕数据学习全身人-物交互：先用个体教师策略精修数据，再扩展规模。
@misc{xu2025intermimic,
title = {{InterMimic: Towards Universal Whole-Body Control for Physics-Based Human-Object Interactions}},
author = {Sirui Xu and Hung Yu Ling and Yu-Xiong Wang and Liang-Yan Gui},
year = {2025},
eprint = {2502.20390},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2502.20390},
}
Learns compliant whole-body control from example motions, so a humanoid yields to unexpected contact instead of stiffly correcting back to the reference.从示例动作学习柔顺全身控制，使人形机器人在意外接触时顺应，而不是僵硬地回到参考动作。
@misc{margolis2025softmimic,
title = {{SoftMimic: Learning Compliant Whole-body Control from Examples}},
author = {Gabriel B. Margolis and Michelle Wang and Nolan Fey and Pulkit Agrawal},
year = {2025},
eprint = {2510.17792},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2510.17792},
}
Integrates impedance control into whole-body tracking so a humanoid's upper body stays compliant in contact with people and objects.将阻抗控制融入全身跟踪，使人形上半身在接触人和物体时保持柔顺。
@misc{lu2025gentlehumanoid,
title = {{GentleHumanoid: Learning Upper-body Compliance for Contact-rich Human and Object Interaction}},
author = {Qingzhou Lu and Yao Feng and Baiyu Shi and Michael Piseno and Zhenan Bao and C. Karen Liu},
year = {2025},
eprint = {2511.04679},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2511.04679},
}
CHIP is a plug-and-play module adding controllable end-effector stiffness to agile tracking policies, for forceful tasks such as wiping and pushing carts.CHIP 是即插即用模块，为敏捷跟踪策略加入可控的末端刚度，用于擦拭、推车等需要施力的任务。
@misc{chen2025chip,
title = {{CHIP: Adaptive Compliance for Humanoid Control through Hindsight Perturbation}},
author = {Sirui Chen and Zi-ang Cao and Zhengyi Luo and Fernando Castañeda and Chenran Li and Tingwu Wang and Ye Yuan and Linxi "Jim" Fan and C. Karen Liu and Yuke Zhu},
year = {2025},
eprint = {2512.14689},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2512.14689},
}
An implicit (decoder-free) world model planned with MPC; one set of hyperparameters works across many continuous-control tasks and scales to a multitask agent.无解码器的隐式世界模型配合 MPC 规划；同一组超参数适用于大量连续控制任务，并可扩展为多任务智能体。
VAMP — vectorizes collision checking with CPU SIMD so sampling-based planners like RRT-Connect solve arm motion-planning queries in microseconds to milliseconds, no GPU needed.VAMP：用 CPU SIMD 指令把碰撞检测向量化，使 RRT-Connect 等基于采样的规划器在微秒到毫秒级求解机械臂运动规划，无需 GPU。
A neural policy trained on millions of planner-generated trajectories that moves an arm to goals straight from point clouds.在数百万条规划器生成的轨迹上训练的神经策略，可直接根据点云把机械臂移动到目标位置。
Survey connecting control-theory and RL views of safety, from learned dynamics with guarantees to constrained RL and safety filters.综述，连接控制理论与强化学习两种安全视角：从带保证的学习动力学到约束强化学习与安全滤波器。
@misc{brunke2021safe,
title = {{Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning}},
author = {Lukas Brunke and Melissa Greeff and Adam W. Hall and Zhaocong Yuan and Siqi Zhou and Jacopo Panerati and Angela P. Schoellig},
year = {2021},
eprint = {2108.06266},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2108.06266},
}
Gym-style benchmark with cart-pole and quadrotor tasks, constraints and disturbances for comparing safe control and safe RL methods.Gym 风格基准，含倒立摆与四旋翼任务、约束与扰动，用于比较安全控制与安全强化学习方法。
@misc{yuan2021safe,
title = {{safe-control-gym: a Unified Benchmark Suite for Safe Learning-based Control and Reinforcement Learning in Robotics}},
author = {Zhaocong Yuan and Adam W. Hall and Siqi Zhou and Lukas Brunke and Melissa Greeff and Jacopo Panerati and Angela P. Schoellig},
year = {2021},
eprint = {2109.06325},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2109.06325},
}
Tutorial on control barrier functions — the standard way to wrap any controller in a minimal-change filter that keeps the system safe.控制障碍函数教程：为任意控制器加一层“尽量少改动”的安全滤波器、保证系统安全的标准方法。
@misc{ames2019control,
title = {{Control Barrier Functions: Theory and Applications}},
author = {Aaron D. Ames and Samuel Coogan and Magnus Egerstedt and Gennaro Notomista and Koushil Sreenath and Paulo Tabuada},
year = {2019},
eprint = {1903.11199},
archivePrefix = {arXiv},
primaryClass = {eess.SY},
url = {https://arxiv.org/abs/1903.11199},
}
RMPs — describe each behaviour (reach, avoid obstacles, joint limits) as an acceleration policy plus a Riemannian metric, then combine them geometrically; the basis of later RMPflow and geometric fabrics controllers.RMP：把每种行为（到达、避障、关节限位）表示为加速度策略加黎曼度量，再按几何方式组合；是后来 RMPflow 与几何织物（geometric fabrics）控制器的基础。
@misc{ratliff2018riemannian,
title = {{Riemannian Motion Policies}},
author = {Nathan D. Ratliff and Jan Issac and Daniel Kappler and Stan Birchfield and Dieter Fox},
year = {2018},
eprint = {1801.02854},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/1801.02854},
}
Short tutorial on Hamilton-Jacobi reachability — computing the set of states from which a system can be kept safe under worst-case disturbances, and the tricks used to scale it up.Hamilton-Jacobi 可达性分析简明教程：计算在最坏扰动下仍能保持系统安全的状态集合，以及将其扩展到更高维系统的方法。
@misc{bansal2017hamilton,
title = {{Hamilton-Jacobi Reachability: A Brief Overview and Recent Advances}},
author = {Somil Bansal and Mo Chen and Sylvia Herbert and Claire J. Tomlin},
year = {2017},
eprint = {1709.07523},
archivePrefix = {arXiv},
primaryClass = {eess.SY},
url = {https://arxiv.org/abs/1709.07523},
}
Yanliang Huang, Peng Xie, Zhen Zhang, Zhuoqi Zeng et al.
Abstract摘要
Autonomous vehicles track planned trajectories close to obstacles. Unknown disturbances make them deviate from these trajectories. With a bound on the tracking error that disturbances can cause, a planned trajectory can be certified as safe before execution. Existing set-based methods bound the tracking error by one invariant ellipsoid for the whole trajectory or by intervals, which are both conservative. For aerial and space vehicles, the tracking error evolves on a matrix Lie group with nonlinear dynamics. However, for group-affine systems, the error dynamics are linear in Lie algebra …
@misc{huang2026certified,
title = {{Certified Zonotopic Reachability Analysis for Trajectory Tracking on Lie Groups}},
author = {Yanliang Huang and Peng Xie and Zhen Zhang and Zhuoqi Zeng and Amr Alanwar},
year = {2026},
eprint = {2611.12767},
archivePrefix = {arXiv},
primaryClass = {eess.SY},
url = {https://arxiv.org/abs/2611.12767},
}
Gabriel da Silva Lima, Made Widhi Surya Atman, Wallace Moreira Bessa
Abstract摘要
Control barrier function (CBF) filters depend on dynamic models that can be inaccurate when their parameters and external forces are uncertain. This paper investigates a radial basis function (RBF) neural network compensator whose output is shared by a Cartesian tracking controller and a joint-space CBF filter. An auxiliary state driven by the difference between requested and applied torques corrects the learning signal, reducing the direct influence of filter intervention on adaptation. A two-link manipulator is evaluated through constrained regulation, ablations, matched uncertainty and …
@misc{lima2026safety,
title = {{Safety-Constrained Intelligent Control of a 2-DOF Robotic Manipulator Using Control Barrier Functions}},
author = {Gabriel da Silva Lima and Made Widhi Surya Atman and Wallace Moreira Bessa},
year = {2026},
eprint = {2611.12378},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.12378},
}
We present and evaluate a game-theoretic model predictive control planner for cooperative goal reaching and adversarial pursuit-evasion with drones. The planner combines 3D prediction, individual objectives, optional shared separation constraints and trajectory-tracking interfaces. A comparison of cooperative game solvers assesses their ability to find feasible trajectories that resolve conflicts. A computational study quantifies how number of drones, prediction horizon and move blocking affect computation time and numerical acceptance. Move blocking holds control inputs constant over groups …
Santiago Berrezueta-Guzman, Habiba-Loai Khalil, Andrei Koshelev, Stefan Wagner
Abstract摘要
Mixed reality (MR) allows users to inspect planned robot motion within their physical surroundings, but displaying trajectories from robotics software requires converting their joint configurations and timing into a format the MR application can play back. This paper presents ROS2MR, an extension of the MR-Robotics LAB platform that converts YAML-serialized ROS 2 joint trajectories, including MoveIt motion plans, into the platform's existing JSON playback format. The converter validates the input, maps arm joints by name, and resamples the trajectory at a nominal rate of 30 Hz, using cubic …
Maitham F. AL-Sunni, Turki Bin Mohaya, John M. Dolan, Peter Seiler
Abstract摘要
Classical optimal control is used to design a separate controller for each plant. This paper asks a different question. Can one learned policy serve a whole family of partially observed, noisy linear systems? We show that one transformer can. The policy is trained to imitate optimal LQG control on a heterogeneous family of Linear Time-Invariant (LTI) systems with Multiple Inputs and Multiple Outputs (MIMO), whose dimensions, cost matrices, and noise levels all vary. A shared representation with system-wise standardization, zero-padding, masking, and an encoding of the cost and noise …
@misc{alsunni2026generalizable,
title = {{Generalizable Output Feedback Optimal Control via Transformers Without Explicit State Estimation}},
author = {Maitham F. AL-Sunni and Turki Bin Mohaya and John M. Dolan and Peter Seiler},
year = {2026},
eprint = {2611.11815},
archivePrefix = {arXiv},
primaryClass = {eess.SY},
url = {https://arxiv.org/abs/2611.11815},
}
Josep Borras-Borras, Pablo Segovia, Carlos Ocampo-Martinez
Abstract摘要
This paper presents a distributed model predictive control (MPC) architecture for collision-avoiding motion planning of planar robots with double-integrator dynamics. To reduce coordination demands, a time-varying predicted conflict graph selects relevant pairwise interactions. The associated collision-avoidance constraints are convexified once per MPC update using warm-start trajectories, yielding a fixed convex quadratic program (QP). The resulting QP is then decomposed using an edge-consensus formulation of the alternating direction method of multipliers (ADMM), enabling parallel …
@misc{borrasborras2026graph,
title = {{Graph-Sparse Distributed Predictive Control for Multi-Robot Motion Planning vianEdge-Consensus ADMM}},
author = {Josep Borras-Borras and Pablo Segovia and Carlos Ocampo-Martinez},
year = {2026},
eprint = {2611.11755},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.11755},
}
Motion generation tools produce candidate trajectories, but interactive robot authoring also requires recoverable execution, traceable edits, explicit geometry, and model-specific validation. We present Kinora, a system that connects text-to-motion generation, a motion library, local agent tools, robot retargeting, and trajectory refinement through versioned artifacts. The current release uses an independently specified 52-joint authoring rig and procedural skeleton previews, separating the distributed human geometry from externally installed generation models and robot assets. Human-motion …
Jaeyoun Choi, Niklas Schmid, Oswin So, Augustinos Saravanos et al.
Abstract摘要
Disturbances encountered during autonomous navigation generally vary across space, often discontinuously, as obstacles redirect wind and changing terrain alters traction. Tracking controllers typically assume sufficient control authority to mitigate disturbances and maintain safety; the regime where this assumption fails remains underexplored. We present Belief-guided Residual Adaptive Control (BRACE), a hierarchical reinforcement learning (RL) framework for reach-avoid navigation under unknown, bounded, spatially varying disturbances. BRACE infers a disturbance belief from model prediction …
@misc{choi2026brace,
title = {{BRACE: Belief-guided Residual Adaptive Control for Safe Navigation under Unknown Disturbances}},
author = {Jaeyoun Choi and Niklas Schmid and Oswin So and Augustinos Saravanos and Chuchu Fan},
year = {2026},
eprint = {2611.10938},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.10938},
}
Sampling-based model predictive control (MPC) is treated as a family of distinct algorithms: predictive sampling, MPPI, the cross-entropy method, and their biased, colored-noise, and annealed variants all sample control sequences and reweight them by cost. We show a single Sequential Monte Carlo (SMC) inference algorithm implements them all, specified by three independent axes: the proposal, the update rule that reduces cost-weighted particles to a nominal plan, and the effective-sample-size threshold for resampling. We evaluate SMC-based planning on MuJoCo non-linear control, open-loop …
@misc{garrett2026sampling,
title = {{Sampling-based Model Predictive Control as Sequential Monte Carlo Inference}},
author = {Austin Garrett and Muyang Yan and Zachary Kingston},
year = {2026},
eprint = {2611.10877},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.10877},
}
Zhihong Cui, Hengyu Liu, Haoran Tang, Shijun Liu et al.
Abstract摘要
Agent harnesses -- runtime scaffolds that wrap an LLM's reasoning loop with tool dispatch, verification, and persistent memory -- have become the standard substrate for deploying LLMs as autonomous agents, as demonstrated by Claude Code and Codex in software engineering. In trajectory planning, however, this paradigm has not taken hold: existing LLM-based trajectory planning methods either keep the LLM in the decision loop, exceeding the millisecond-level control budget, or offload it to offline rules or training-time supervision -- leaving the runtime planner non-evolving. Intermediate …
@misc{cui2026reins,
title = {{REINS: A Self-Evolving Agent Harness for Real-Time Trajectory Planning}},
author = {Zhihong Cui and Hengyu Liu and Haoran Tang and Shijun Liu and Amir Taherkordi and Tor Skeie},
year = {2026},
eprint = {2611.10748},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.10748},
}
Zhihong Cui, Hengyu Liu, Michael Alexander Riegler, Guandong Xu et al.
Abstract摘要
Vision-Language Models (VLMs) are increasingly adopted as end-to-end driving planners, yet they suffer from cross-city domain shift. Existing methods treat this shift as a single quantity addressed by uniform alignment or single-site editing. We challenge this view on two counts: (i) domain shift in VLM trajectory planners is inherently structured across modality and layer, and (ii) a locate-then-repair recipe matched to this structure outperforms every uniform-objective baseline. We analyse domain shift via activation patching and uncover two regularities. (F1) Modality axis. Image and text …
@misc{cui2026locating,
title = {{Locating and Repairing Domain Shift in VLM Trajectory Planning}},
author = {Zhihong Cui and Hengyu Liu and Michael Alexander Riegler and Guandong Xu and Amir Taherkordi and Tor Skeie},
year = {2026},
eprint = {2611.10720},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2611.10720},
}
Multi-Agent Path Finding (MAPF) seeks collision-free paths for multiple agents from their respective starting locations to their respective goal locations while minimizing path costs. Although many MAPF algorithms were developed, most of them rely on a common assumption on synchronized actions, where the actions of all agents start at the same time and always take a time unit. This assumption may limit the use of MAPF planners in practice. To get rid of this assumption, recently, an algorithm called Loosely Synchronized Rule-based Planning (LSRP) was proposed, which can find sub-optimal …
@misc{liu2026lsrp,
title = {{LSRP*: Scalable and Anytime Planning for Multi-Agent Path Finding with Asynchronous Actions}},
author = {Dachuan Liu and Shuai Zhou and Shizhe Zhao and Zhongqiang Ren},
year = {2026},
eprint = {2611.10363},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.10363},
}
Athanasios Kyriakopoulos, Shubham S. Kumbhar, Kunal S. Narkhede, Abhijeet M. Kulkarni et al.
Abstract摘要
A fundamental aspect of humanoid footstep planning is to jointly guarantee obstacle avoidance and locomotion stability. This paper presents a capturability-aware sequential Model Predictive Control (MPC) framework for Linear Inverted Pendulum (LIP)-based humanoid footstep planning in the presence of obstacles. The proposed method combines obstacle avoidance with capturability constraints through safe corridors constructed as sequences of intersecting rectangles, yielding a sequential MPC framework with recursive feasibility guarantees for the LIP-based planning layer. The planner autonomously …
@misc{kyriakopoulos2026capturability,
title = {{Capturability-Aware Footstep Planning in Cluttered Environments: A Recursively Feasible Sequential MPC on Axis-Aligned Safe Corridors}},
author = {Athanasios Kyriakopoulos and Shubham S. Kumbhar and Kunal S. Narkhede and Abhijeet M. Kulkarni and Ioannis Poulakakis},
year = {2026},
eprint = {2611.10169},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.10169},
}
Jianghan Zhang, Roland Schwan, Gabriel Bravo-Palacios, John Zhang et al.
Abstract摘要
Differentiable model predictive control (MPC) enables structured, constraint-aware policies to be optimized end-to-end. However, learning MPC policies faces two bottlenecks: (1) nonsmooth sensitivities at active-set transitions and (2) expensive policy evaluation, since each requires an optimization solve. We first introduce SmoothedTurboMPC, a GPU-parallel differentiable MPC solver combining sequential quadratic programming (SQP) with a log-barrier-smoothed alternating direction method of multipliers (ADMM) inner solver. Its design enables smooth gradients, exact sensitivities at …
@misc{zhang2026efficient,
title = {{Efficient Learning of Differentiable MPC Policies on the GPU via Smoothed ADMM and First-Order Reinforcement Learning}},
author = {Jianghan Zhang and Roland Schwan and Gabriel Bravo-Palacios and John Zhang and Thomas Lew and Brian Plancher},
year = {2026},
eprint = {2611.09937},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.09937},
}
Autonomous racing provides a demanding testbed for collision avoidance under tight geometric and computational constraints. Building on a recent conference contribution and a master's dissertation, LACART combines lookahead-based control barrier functions (CBFs), a relaxed control Lyapunov function (CLF), and quadratic programming (QP) to modify nominal vehicle commands for obstacle avoidance and reference tracking. The method handles circular obstacles and track boundaries in a common optimisation layer. Comparative simulation results against Huang \emph{et al.} \cite{r9} show similar …
Jose Luis Blanco-Claraco, Shravan S. Rai, Fernando Cañadas-Aránega, José Luis Torres-Moreno et al.
Abstract摘要
Global planners for ground vehicles usually approximate the vehicle by a disk, trading safety against conservativeness, while exact-footprint checking pays for every candidate motion, pose, and footprint edge. We present TPS-A*, a lattice planner whose edges are kinematically feasible trajectories from parameterized trajectory generators, built on certified collision checking in trajectory parameter space. A grid precomputed per footprint returns, in one pass over the local obstacles, a certified free distance along every candidate trajectory, so the per-node collision cost is independent of …
@misc{blancoclaraco2026tps,
title = {{TPS-A*: Path Planning for Any-Shape Vehicles with Certified Collision Checking in Trajectory Parameter Space}},
author = {Jose Luis Blanco-Claraco and Shravan S. Rai and Fernando Cañadas-Aránega and José Luis Torres-Moreno and José Carlos Moreno-Úbeda},
year = {2026},
eprint = {2611.09210},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.09210},
}
In recent years, massively parallelized rigid-body simulation has played a central role in robotics research, enabling rapid progress in areas such as reinforcement learning, stochastic optimal control, and randomized smoothing-based trajectory optimization. Particularly in contact-rich settings, the scalability of such simulations in large part depends on well-optimized representations of collision geometry, often requiring a coarse approximation of the underlying surfaces using primitives such as spheres, capsules, cylinders, and boxes. Whenever the complexity of the surfaces or the …
@misc{beker2026lodos,
title = {{Lodos: A Lightweight Simulator Designed for Contact-Rich Interactions with Detailed Meshes}},
author = {Onur Beker and Andreas René Geist and Georg Martius},
year = {2026},
eprint = {2611.08872},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.08872},
}
Repeated directed reachability queries arise in online path planning, task-agent allocation, and swarm control on static grid maps, where reachability is asymmetric and full transitive closure is storage-prohibitive. We introduce H-BRICK, a Hierarchical Boundary Reachability Index with Compressed Kleene closure for exact cell-to-cell queries on directed grid graphs. H-BRICK partitions the graph into tiles, computes a local Boolean Kleene closure within each tile, and projects it onto the tile's boundary ports. The resulting directed port-to-port summaries are then composed recursively across …
Adnan Takriti, Mahmoud Sadeq, Quentin Rouxel, Daniele Cafolla et al.
Abstract摘要
Physical human--robot interaction requires controllers capable of maintaining intrinsically bounded interactions with the environment. The Fractal Impedance Controller (FIC) has been proven to guarantee these safety characteristics by design. This paper introduces the robotic implementation and experimental validation of its most advanced architecture: the Harmonic-Passive Motion Paradigm (H-PMP), an evolution of the Passive Motion Paradigm designed to mimic human-like interaction. Furthermore, we present a unified framework for FIC deployment, supported by an open-source software suite …
@misc{takriti2026unified,
title = {{A Unified Fractal Impedance Control Framework}},
author = {Adnan Takriti and Mahmoud Sadeq and Quentin Rouxel and Daniele Cafolla and Michael Mistry and Carlo Tiseo},
year = {2026},
eprint = {2611.08009},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.08009},
}
Hao Wang, Max Cohen, Zeyuan Feng, Donggeon David Oh et al.
Abstract摘要
Safety is a central concern in modern autonomous systems. Safety certificates---functions whose existence guarantees that a system meets its safety specification---have long been used as a principled tool for rigorously reasoning about safety. Methods for synthesizing such certificates are scattered across research areas, with little guidance on when to use a particular approach. This tutorial provides a holistic treatment of safety certificate synthesis centered on two widely used families: Hamilton--Jacobi (HJ) reachability analysis and control barrier functions (CBFs). For each family of …
@misc{wang2026unified,
title = {{Unified Overview of Safety Certificate Functions: Techniques from Hamilton--Jacobi Reachability and Control Barrier Functions}},
author = {Hao Wang and Max Cohen and Zeyuan Feng and Donggeon David Oh and Oswin So and Songyuan Zhang and Javier Borquez and Chuchu Fan and others},
year = {2026},
eprint = {2611.07537},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.07537},
}
Balakumar Sundaralingam, Adithyavairavan Murali, Stan Birchfield
Abstract摘要
We present cuRoboV2-Fast, a GPU-accelerated planner that reduces both planning latency and execution time for collision-free, payload-aware, torque-limited motions. B-spline trajectory optimization reduces the required iterations, while accelerated robot computations and differentiable inverse dynamics reduce refinement cost and accommodate changing payloads. TimeOpt optimizes traversal speed along the fixed path under sampled velocity, acceleration, jerk, and torque constraints, exploiting local spline support to solve banded Newton systems efficiently on the GPU. With a 3,kg payload, …
Kendra Givens, Taylor T. Johnson, Bryan C. Ward, Thomas Beckers
Abstract摘要
Hamilton-Jacobi (HJ) Reachability identifies the unsafe states of a dynamical system from which it can be forced into failure under worst-case disturbance. However, classical grid-based solvers scale exponentially with state dimension. DeepReach improves scalability by replacing the grid with a neural value function trained on the HJ-Isaacs (HJI) variational inequality (VI), but penalizes both sides of the residual equally. This allows the learned value to lie above the true one and unsafe states to be misclassified as safe. We propose Conservative DeepReach (ConDR) which aims to learn a …
@misc{givens2026condr,
title = {{ConDR: Conservative Reachability through Neural Hamilton-Jacobi Subsolutions}},
author = {Kendra Givens and Taylor T. Johnson and Bryan C. Ward and Thomas Beckers},
year = {2026},
eprint = {2611.06802},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.06802},
}
Duo Tang, Ping Deng, Sophie Armanini, Yajing Shen et al.
Abstract摘要
Inspired by the coordination of global motion and local shape adaptation in snakes, elephant trunks, and octopus arms, we propose a unified planning and control framework for mobile continuum robots in cluttered and confined environments. Existing approaches often address high-level path planning and local motion control separately, limiting coordinated base-arm motion, kinematic consistency, and collision avoidance. Our framework integrates long-horizon trajectory planning with real-time geometric refinement under kinematic and environmental constraints. The global layer combines A* search …
@misc{tang2026riemannian,
title = {{Riemannian Optimization-based Whole-body Obstacle Avoidance for Mobile Continuum Robots in Cluttered Environments}},
author = {Duo Tang and Ping Deng and Sophie Armanini and Yajing Shen and Toshio Fukuda and Cosimo Della Santina and Peng Lu},
year = {2026},
eprint = {2611.06214},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.06214},
}
Gianni Lunardi, Elias Fontanari, Matteo Saveriano, Andrea Del Prete
Abstract摘要
Ensuring safety in advanced robot control methods such as Model Predictive Control (MPC) and Reinforcement Learning remains an open challenge. A common approach relies on control-invariant sets, yet their exact computation for nonlinear systems is generally intractable, and numerical approximations compromise theoretical guarantees. In this work, we show that safety does not need to rely exclusively on control invariance. We introduce novel classes of safe sets, each broader than classical control-invariant sets, and analyze corresponding MPC schemes that guarantee recursive feasibility by …
@misc{lunardi2026beyond,
title = {{Beyond Terminal Constraints: Reliable Robot Control with Learned Safe Sets}},
author = {Gianni Lunardi and Elias Fontanari and Matteo Saveriano and Andrea Del Prete},
year = {2026},
eprint = {2611.06196},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.06196},
}
Short-horizon model predictive control (MPC) is fast but myopic. ZipMPC compensates by learning a context-dependent correction of the short-horizon cost that imitates a long-horizon expert. We observe that the optimal correction is often multimodal: when a slower opponent can be passed on either side, good corrections of opposite sign coexist, and the expert's choice between them hinges on tiny cost differences. Deterministic regression then averages modes or inherits inconsistent expert tie-breaking. We propose ZipDrift, which trains a one-step drifting generator over MPC cost corrections, …
Reinhard M. Grassmann, Abdulaziz Y. Alkayas, Lingyun Chen, Sami Haddadin
Abstract摘要
High motion performance and avoiding structural vibrations rely on smooth trajectory planning. The smoothness requirement depends on the robot's complexity in terms of the number of degrees of freedom and system order due to the flexibility of the joints and links. For industrial robots, the smoothness requirements have been determined and subsequently taken into account. Although unknown for soft continuum robots, the exact smoothness requirement can potentially be much larger than previously considered ones such as $\mathbf{\mathcal{C}^4}$ for collaborative robots and …
@misc{grassmann2026mathcal,
title = {{$\mathcal{C}^\infty$ Smooth Trajectory Planning with Application to Soft Robots}},
author = {Reinhard M. Grassmann and Abdulaziz Y. Alkayas and Lingyun Chen and Sami Haddadin},
year = {2026},
eprint = {2611.05615},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.05615},
}
Roberto Mauceri, Stefano Dafarra, Giulio Romualdi, Marco Gabiccini et al.
Abstract摘要
This letter presents a trajectory planner for humanoid robots to perform dynamic motions on complex, parameterizable terrains using a contact-implicit whole-body optimization framework. The planner generates feasible movements without predefined contact sequences, considering the robot's full dynamics and kinematics. To plan bipedal locomotion on steps, ramps, and other terrains, we propose a mathematical model to approximate ground geometry with a smooth surface. The rigid interaction between the robot and the ground is modeled via novel contact constraints, extending the domain of …
Multi-Agent Reinforcement Learning (MARL) shows promise in coordinating connected and automated vehicles but offers no safety guarantee. Although Control Barrier Functions (CBFs) can add this guarantee, their optimization problem can become infeasible in practice due to control bounds. Moreover, they often overapproximate vehicle and road geometry, which makes them conservative. This paper proposes the Backup-Horizon CBF (BH-CBF), which evaluates safety along a horizon until all vehicles stop. BH-CBF is recursively feasible under control bounds, and it considers the exact vehicle and road …
Hamilton-Jacobi (HJ) reachability provides certified backward reachable tubes (BRTs) for safety-critical robotic systems, but the level-set computation scales exponentially in the state dimension and has historically been performed offline. As autonomy stacks (ADAS/AV, humanoid robotics, and vision-language-action models) increasingly rely on black-box, probabilistic policies, a deterministic safety layer with formal guarantees is an essential complement. We present a CUDA implementation of the canonical WENO5 + TVD-RK3 + Lax-Friedrichs level-set Hamilton-Jacobi-Isaacs solver for 2D, 3D, and …
Ball-balancing robots (ballbots) must lean to accelerate, and the lean is limited by the wheel torques and by safety. Planners that use the full robot model are expensive, while planners that ignore the balance dynamics produce references that cannot be followed. This paper presents a two-layer navigation scheme for a three-omni-wheel ballbot. The upper layer is a model predictive controller on a \emph{jerk model} of the ball position, with discrete-time control barrier functions for static, moving, rotating and concave obstacles. The lower layer is a hierarchical sliding mode controller with …
Control barrier functions (CBFs) are a standard way to keep a planner inside a safe set, but they assume control affine dynamics, a barrier function given in closed form, and a known nominal controller. Integral control barrier functions (ICBFs) remove the first assumption and absorb input constraints, yet they still assume a given nominal controller and a barrier function. In this work, we present an approach to learn all three from data, using neural ordinary differential equations (ODEs) for the dynamics, imitation for the nominal controller, and supervised regression for the barrier …
Safe navigation in interactive traffic requires autonomous vehicles to reason about unobservable human characteristics and the reciprocal nature of multi-agent interactions. Traditional architectures often decouple prediction from planning, leading to the "Frozen Robot Problem" or unsafe maneuvers under high ambiguity because they treat risk assessment and information acquisition as independent objectives. We argue that the notion of safety should not remain static but must instead dynamically co-evolve with the system's internal confidence: the robot should actively clarify ambiguity when …
@misc{wang2026when,
title = {{When to Probe and When to Act: Clarity-Aware Risk-Adaptive Control for Interactive Driving}},
author = {Youran Wang and Devodita Chakravarty and Yiwei Lyu},
year = {2026},
eprint = {2611.02240},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.02240},
}
In human-robot co-manipulation, the robot's trajectory should accommodate the human's, where human sensorimotor behavior is affected by the task's spatial and temporal constraints, as well as personal factors. Several robot trajectory planning methods have been proposed to embed human sensorimotor models in the robot trajectory planning, but these methods do not consider temporal constraints that adapt to how the human behaves when interacting with the robot. To better align human and robot collaborative motion, this work adapts a stochastic optimal control problem with a final state …
@misc{hall2026human,
title = {{Human Sensorimotor Model Adaptation to Account for Robot Accuracy and Speed Augmentation in Human-Robot Co-Manipulation}},
author = {Frits van Hall and Kevin Haninger and Luka Peternel},
year = {2026},
eprint = {2611.02181},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.02181},
}
Latent-space path planning (LSPP) compresses the configuration space of a robotic manipulator into a compact, differentiable manifold learned by a variational autoencoder, enabling fast gradient-based planning without explicit inverse kinematics. Existing LSPP frameworks enforce obstacle avoidance with a binary collision classifier, whose saturated gradients near obstacle boundaries and lack of temporal structure cause the optimizer to stall and provide no forward-invariance guarantee. We replace the classifier with a neural control barrier function (CBF) learned directly in the latent space. …
End-to-end planners decode the ego trajectory either by coordinate regression, which is fast but blends plausible maneuvers into one trajectory that may follow none of them, or by diffusion and flow matching, which model the trajectory distribution but need several inference steps. Drifting models move this iteration into training: a one-step generator learns to follow an attraction--repulsion field between generated and ground-truth samples. Drifting was developed for class-conditional image generation, where every class supplies many positive samples. In planning, every scene is its own …
@misc{liu2026driftingplanner,
title = {{DriftingPlanner: Drifting Generative Models for One-Step Trajectory Planning in End-to-End Autonomous Driving}},
author = {Ziming Liu and Yizhan Qu and Leichen Wang and Hao Sun},
year = {2026},
eprint = {2611.01283},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2611.01283},
}
Mohammed Al-Ghaili, Yakoop Qasim, Hassan Almubarak
Abstract摘要
High-accuracy repetitive tracking is challenging for position-commanded manipulators whose low-level controllers are inaccessible, while constrained model-based iterative learning control (ILC) can require substantial manual parameter tuning. We present Differentiable Iterative Learning Control (Diff-ILC), an online bilevel framework that adapts the parameters of a constrained ILC update between physical robot executions. A closed-loop differential model is identified once from paired smooth perturbations and subsequently kept fixed. At each trial, a constrained quadratic program computes a …
Autonomous wheelchairs can improve the quality of life for individuals with mobility impairments by providing greater independence. However, reliable navigation especially in human environments is a major challenge due to factors such as dynamic obstacles, unpredictable pedestrian behavior, and the users' need to complete multiple stops along the way before reaching their final destination. This paper presents a hybrid path planning framework for multi-goal navigation in dynamic environments. The framework integrates the Traveling Salesman Problem (TSP) for global task ordering with the …
Elia Trevisan, Alejandro Gonzalez-Garcia, Jan Swevers, Javier Alonso-Mora
Abstract摘要
Autonomous surface vessels operating in urban waterways must coordinate with other vessels in confined spaces while complying with traffic regulations, often without knowing their intentions. We present a decentralized motion planner based on model predictive path integral control, a sampling-based model predictive control scheme. The planner jointly computes a trajectory for the ego vessel and a prediction of the trajectories of the other vessels, and therefore accounts for their interaction. Traffic regulations are encoded as discontinuous costs, which the sampling-based formulation admits …
@misc{trevisan2026interaction,
title = {{Interaction-Aware Sampling-Based Motion Planning for Autonomous Surface Vessels in Urban Waterways}},
author = {Elia Trevisan and Alejandro Gonzalez-Garcia and Jan Swevers and Javier Alonso-Mora},
year = {2026},
eprint = {2610.41676},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.41676},
}
Yizhou Chen, Shupeng Lai, Jinqiang Cui, Biao Wang et al.
Abstract摘要
In this letter, we present a volumetric mapping system that effectively calculates Occupancy Grid Maps (OGMs) and Euclidean Distance Transforms (EDTs) with parallel computing. Unlike these mappers for high-precision structural reconstruction, our system incrementally constructs global EDT and outputs high-frequency local distance information for online robot motion planning. The proposed system constructs OGM with massive amount of data from multiple types of sensor inputs. Using GPU programming techniques, the system quickly computes EDT in parallel within local volume. The new observation …
@misc{chen2026gpu,
title = {{GPU-accelerated Incremental Euclidean Distance Transform for Online Motion Planning of Mobile Robots}},
author = {Yizhou Chen and Shupeng Lai and Jinqiang Cui and Biao Wang and Ben M. Chen},
year = {2026},
eprint = {2610.41667},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.41667},
}
Collision-free motion planning for multi-agent robotic systems remains an active research area because of the complexity of coordinating robot trajectories, with applications ranging from coordinated drone shows to autonomous operations on land, in the air, and on water. Importantly, the analytical difficulty of nonconvex collision-avoidance constraints has so far driven the development of exclusively numerical methods. We derive a complete analytical characterization of the optimal trajectories for two robots that start and end at rest, under double-integrator dynamics and a …
@misc{kamm2026complete,
title = {{A complete analytical characterization of optimal collision-free motion for two robots}},
author = {Jan Kamm and Antonio Terpin and Raffaello D'Andrea},
year = {2026},
eprint = {2610.41558},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.41558},
}