Interaction-Aware Whole-Body Control for Compliant Object Transport
Department of Electrical Engineering, the University of Texas at Arlington（电气工程系，德克萨斯大学阿灵顿分校）；Department of Mechanical Engineering, Carnegie Mellon University（机械工程系，卡内基梅隆大学）
Cooperative object transport in unstructured environments remains challenging for assistive humanoids because strong, time-varying interaction forces can make tracking-centric whole-body control unreliable, especially in close-contact support tasks. This paper proposes a bio-inspired, interaction-oriented whole-body control (IO-WBC) that functions as an artificial cerebellum - an adaptive motor agent that translates upstream (skill-level) commands into stable, physically consistent whole-body behavior under contact. This work structurally separates upper-body interaction execution from lower-body support control, enabling the robot to maintain balance while shaping force exchange in a tightly coupled robot-object system. A trajectory-optimized reference generator (RG) provides a kinematic prior, while a reinforcement learning (RL) policy governs body responses under heavy-load interactions and disturbances. The policy is trained in simulation with randomized payload mass/inertia and external perturbations, and deployed via asymmetric teacher-student distillation so that the student relies only on proprioceptive histories at runtime. Extensive experiments demonstrate that IO-WBC maintains stable whole-body behavior and physical interaction even when precise velocity tracking becomes infeasible, enabling compliant object transport across a wide range of scenarios.
Istituto Italiano di Tecnologia (IIT)（意大利理工学院）；LAAS-CNRS（法国国家科学研究中心自动化系统实验室）
We present experimental results using a passive whole-body control approach for quadruped robots that achieves dynamic locomotion while compliantly balancing the robot's trunk. We formulate the motion tracking as a Quadratic Program (QP) that takes into account the full robot rigid body dynamics, the actuation limits, the joint limits and the contact interaction. We analyze the controller's robustness against inaccurate friction coefficient estimates and unstable footholds, as well as its capability to redistribute the load as a consequence of enforcing actuation limits. Additionally, we prese…
Northeastern University（东北大学）；United States Coast Guard（美国海岸警卫队）；MIT - WHOI Joint Program（麻省理工学院-伍兹霍尔海洋研究所联合项目）；Lilium GmbH（莉莉姆公司）
In this paper, we look at the role of autonomous navigation in the maritime domain. Specifically, we examine how an Autonomous Surface Vessel(ASV) can achieve obstacle avoidance based on the Convention on the International Regulations for Preventing Collisions at Sea (1972), or COLREGs, in real-world environments. Our ASV is equipped with a broadband marine radar, an Inertial Navigation System (INS), and uses official Electronic Navigational Charts (ENC). These sensors are used to provide situational awareness and, in series of well-defined steps, we can exclude land objects from the radar dat…
A Synergistic Framework for Learning Shape Estimation and Shape-Aware Whole-Body Control Policy for Continuum Robots
School of Informatics, University of Edinburgh（爱丁堡大学信息学院）；Walker Department of Mechanical Engineering, the University of Texas at Austin（德克萨斯大学奥斯汀分校沃克机械工程系）
Learning Human-Humanoid Coordination for Collaborative Object Carrying
Department of Electrical and Electronic Engineering, the University of Hong Kong（香港大学电子与电气工程系）；State Key Laboratory of General Artificial Intelligence, BIGAI（通用人工智能国家重点实验室，BIGAI）；School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；Yuanpei College, Peking University（北京大学元培学院）
In unstructured urban canals, regulation-aware interactions with other vessels are essential for collision avoidance and social compliance. In this paper, we propose a regulations aware motion planning framework for Autonomous Surface Vessels (ASVs) that accounts for dynamic and static obstacles. Our method builds upon local model predictive contouring control (LMPCC) to generate motion plans satisfying kino-dynamic and collision constraints in real-time while including regulation awareness. To incorporate regulations in the planning stage, we propose a cost function encouraging compliance wit…
HUSKY: Humanoid Skateboarding System via Physics-Aware Whole-Body Control
Institute of Artificial Intelligence (TeleAI), China Telecom（人工智能研究院（TeleAI），中国电信）；The University of Hong Kong（香港大学）；University of Science and Technology of China（中国科学技术大学）；ShanghaiTech University（上海科技大学）
Istituto Italiano di Tecnologia（意大利技术研究院）；University of Edinburgh（爱丁堡大学）；University of Oxford（牛津大学）
Planning whole-body motions while taking into account the terrain conditions is a challenging problem for legged robots since the terrain model might produce many local minima. Our coupled planning method uses stochastic and derivatives-free search to plan both foothold locations and horizontal motions due to the local minima produced by the terrain model. It jointly optimizes body motion, step duration and foothold selection, and it models the terrain as a cost-map. Due to the novel attitude planning method, the horizontal motion plans can be applied to various terrain conditions. The attitud…
Physics Residual Dynamics and Reduced Order Whole-Body Planning for Obstacle Aware Human Robot Cloth CoTransportation
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·扎耶德人工智能大学）；Technical University of Munich (TUM)（慕尼黑工业大学）；Tampere University（坦佩雷大学）；Aalto University（阿尔托大学）
Massachusetts Institute of Technology（麻省理工学院）；IBM Thomas J. Watson Research Center（IBM托马斯·J·沃森研究中心）
For robotic vehicles to navigate safely and efficiently in pedestrian-rich environments, it is important to model subtle human behaviors and navigation rules (e.g., passing on the right). However, while instinctive to humans, socially compliant navigation is still difficult to quantify due to the stochasticity in people's behaviors. Existing works are mostly focused on using feature-matching techniques to describe and imitate human paths, but often do not generalize well since the feature values can vary from person to person, and even run to run. This work notes that while it is challenging t…
Dynamic Modeling and MPC for Locomotion of Tendon-Driven Soft Quadruped
Department of Electrical Engineering, IIT Gandhinagar（电子工程系，印度加尔各直辖区理工学院）；Department of Electronics and Computer Engineering, Sreenidhi Institute of Science and Technology（电子与计算机工程系，斯里尼迪学院科学与技术研究所）；Department of Mechanical Engineering, IIT Gandhinagar（机械工程系，印度加尔各直辖区理工学院）
Learning a Kinodynamic Trajectory Manifold for Impact-Aware Compliant Catching of Fast-Moving Objects
College of Robotics（机器人学院）；Taiyuan University of Technology（太原科技大学）；School of Data Science（数据科学学院）；City University of Hong Kong (Dongguan)（香港城市大学（东莞））；School of Advanced Engineering（先进工程学院）；Great Bay University（大湾大学）；Department of Mechanical Engineering（机械工程系）；The Hong Kong Polytechnic University（香港理工大学）；College of Mechanical and Electrical Engineering（机械与电子工程学院）；Northeast Forestry University（东北林业大学）
Regulation-Aware Game-Theoretic Motion Planning for Autonomous Racing
University of Modena and Reggio Emilia（摩德纳和雷吉奥艾米利亚大学）；University of Bologna（博洛尼亚大学）；University of California, Berkeley（加州大学伯克利分校）；Chalmers University of Technology（查尔姆斯理工大学）
Whole Body Model Predictive Control for Spin-Aware Quadrupedal Table Tennis
Department of Mechanical Engineering, Massachusetts Institute of Technology（麻省理工学院机械工程系）；Department of Mechanical Engineering, Georgia Institute of Technology（佐治亚理工学院机械工程系）；RAI Institute（RAI研究所）
Toyota Research Institute（丰田研究院）；Stanford University（斯坦福大学）
Multibody simulation with frictional contact has been a challenging subject of research for the past thirty years. Rigid-body assumptions are commonly used to approximate the physics of contact, and together with Coulomb friction, lead to challenging-to-solve nonlinear complementarity problems (NCP). On the other hand, robot grippers often introduce significant compliance. Compliant contact, combined with regularized friction, can be modeled entirely with ODEs, avoiding NCP solves. Unfortunately, regularized friction introduces high-frequency stiff dynamics and even implicit methods struggle w…
Interaction-Aware Model Predictive Decision-Making for Socially-Compliant Autonomous Driving in Mixed Urban Traffic Scenarios
Institute of Control Systems, Karlsruhe Institute for Technology（控制系统研究所，卡尔斯鲁厄技术学院）；Wuerzburg Institute for Traffic Sciences GmbH（魏玛交通科学研究所有限公司）；Faculty of Engineering - Mechanical Engineering Dept, University of Alberta（工程学院-机械工程系，阿尔伯塔大学）
Blind Dexterity: Whole-Body Humanoid Manipulation via Pure Proprioception
TU Darmstadt（达姆施塔特工业大学）；German Research Center for AI (DFKI)（德国人工智能研究中心）；Tongji University（同济大学）；Shanghai Research Institute for Autonomous Intelligent Systems（上海自主智能系统研究院）；Robotics Institute Germany（德国机器人研究所）
Control of Powered Ankle-Foot Prostheses on Compliant Terrain: A Quantitative Approach to Stability Enhancement
Mechanical Engineering Department, at the University of Delaware（德克萨斯大学达勒姆分校机械工程系）；Mechanical Engineering Department, at the Rice University（赖斯大学机械工程系）
SAC-Loco: Safe and Adjustable Compliant Quadrupedal Locomotion
Department of Electrical and Computer Engineering, National University of Singapore（新加坡国立大学电子与计算机工程系）；School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）
Istituto Italiano di Tecnologia（意大利理工学院）；ETH Zurich（苏黎世联邦理工学院）
We present a framework for dynamic quadrupedal locomotion over challenging terrain, where the choice of appropriate footholds is crucial for the success of the behaviour. We build a model of the environment on-line and on-board using an efficient occupancy grid representation. We use Any-time-Repairing A* (ARA*) to search over a tree of possible actions, choose a rough body path and select the locally-best footholds accordingly. We run a n-step lookahead optimization of the body trajectory using a dynamic stability metric, the Zero Moment Point (ZMP), that generates natural dynamic whole-body …
PhysiFlow: Physics-Aware Humanoid Whole-Body VLA via Multi-Brain Latent Flow Matching and Robust Tracking
department of Automation Engineering, Shanghai Jiao Tong University（自动化工程系，上海交通大学）；Department of Automation, Key Laboratory of System Control and Information Processing of Ministry of Education, Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education, Shanghai Engineering Research Center of Intelligent Control and Management, Shanghai Jiao Tong University（自动化系，教育部系统控制与信息处理重点实验室，教育部海洋智能设备与系统重点实验室，智能控制与管理上海工程研究中心，上海交通大学）
