In this paper, we propose a novel inverse Dynamic Reachability Map (iDRM) that allows a floating base system to find valid end-poses in complex and dynamically changing environments in real-time. End-pose planning for valid stance pose and collision-free configuration is an essential problem for humanoid applications, such as providing goal states for walking and motion planners. However, this is non-trivial in complex environments, where standing locations and reaching postures are restricted by obstacles. Our proposed iDRM customizes the robot-to-workspace occupation list and uses an online update algorithm to enable efficient reconstruction of the reachability map to guarantee that the selected end-poses are always collision-free. The iDRM was evaluated in a variety of reaching tasks using the 38 degree-of-freedom (DoF) humanoid robot Valkyrie. Our results show that the approach is capable of finding valid end-poses in a fraction of a second. Significantly, we also demonstrate that motion planning algorithms integrating our end-pose planning method are more efficient than those not utilizing this technique.
Planning balanced and collision-free motion for humanoid robots is non-trivial, especially when they are operated in complex environments, such as reaching targets behind obstacles or through narrow passages. We propose a method that allows us to apply existing sampling--based algorithms to plan trajectories for humanoids by utilizing a customized state space representation, biased sampling strategies, and a steering function based on a robust inverse kinematics solver. Our approach requires no prior offline computation, thus one can easily transfer the work to new robot platforms. We tested t…
Learning Differentiable Reachability Maps for Optimization-based Humanoid Motion Generation
CNRS-AIST JRL (Joint Robotics Laboratory)（法国国家科学研究中心-日本产业技术综合研究所联合机器人实验室）；IRL（智能机器人实验室）；National Institute of Advanced Industrial Science and Technology (AIST)（国家先进工业科学与技术研究所）
This paper presents a multi-contact approach to generalized humanoid fall mitigation planning that unifies inertial shaping, protective stepping, and hand contact strategies. The planner optimizes both the contact sequence and the robot state trajectories. A high-level tree search is conducted to iteratively grow a contact transition tree. At each edge of the tree, trajectory optimization is used to calculate robot stabilization trajectories that produce the desired contact transition while minimizing kinetic energy. Also, at each node of the tree, the optimizer attempts to find a self-motion …
Institute of Human and Machine Cognition（人类与机器认知研究所）；University of West Florida（西佛罗里达大学）；Pusan National University（釜山国立大学）；Boardwalk Robotics, Inc.（Boardwalk机器人公司）
We present a planning framework designed for humanoid navigation over challenging terrain. This framework is designed to plan a traversable, smooth, and collision-free path using a 2.5D height map. The planner is comprised of two stages. The first stage consists of an A* planner which reasons about traversability using terrain features. A novel cost function is presented which encodes the bipedal gait directly into the graph structure, enabling natural paths that are robust to small gaps in traversability. The second stage is an optimization framework which smooths the path while further impro…
Istituto Italiano di Tecnologia（意大利理工学院）；Università degli Studi di Genova（热那亚大学）；Google DeepMind（谷歌DeepMind）
A common approach to the generation of walking patterns for humanoid robots consists in adopting a layered control architecture. This paper proposes an architecture composed of three nested control loops. The outer loop exploits a robot kinematic model to plan the footstep positions. In the mid layer, a predictive controller generates a Center of Mass trajectory according to the well-known table-cart model. Through a whole-body inverse kinematics algorithm, we can define joint references for position controlled walking. The outcomes of these two loops are then interpreted as inputs of a stack-…
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Massachusetts Institute of Technology（麻省理工学院）
Demonstrating acrobatic behavior of a humanoid robot such as flips and spinning jumps requires systematic approaches across hardware design, motion planning, and control. In this paper, we present a new humanoid robot design, an actuator-aware kino-dynamic motion planner, and a landing controller as part of a practical system design for highly dynamic motion control of the humanoid robot. To achieve the impulsive motions, we develop two new proprioceptive actuators and experimentally evaluate their performance using our custom-designed dynamometer. The actuator's torque, velocity, and power li…
Ecole Centrale de Lille（里尔中央理工学院）；Beihang University（北京航空航天大学）
A high redundant non-holonomic humanoid mobile dual-arm manipulator system is presented in this paper where the motion planning to realize "human-like" autonomous navigation and manipulation tasks is studied. Firstly, an improved MaxiMin NSGA-II algorithm, which optimizes five objective functions to solve the problems of singularity, redundancy, and coupling between mobile base and manipulator simultaneously, is proposed to design the optimal pose to manipulate the target object. Then, in order to link the initial pose and that optimal pose, an off-line motion planning algorithm is designed. I…
Humanoid Locomotion and Manipulation: Current Progress and Challenges in Control, Planning, and Learning
Georgia Institute of Technology（佐治亚理工学院）；The University of Southern California（南加州大学）；Technische Universität München（慕尼黑技术大学）；Google DeepMind（谷歌DeepMind）；The AI Institute（人工智能研究所）；The Institute for Human and Machine Cognition（人机认知研究所）；Duke University（杜克大学）；Kuwait University（科威特大学）；Stanford University（斯坦福大学）；CNRS-University of Montpellier（国家科学研究中心-蒙彼利埃大学）；LIRMM CNRS-AIST Joint Robotics Laboratory（联合机器人实验室）；Simon Fraser University（西蒙弗雷泽大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；NVIDIA（英伟达）；Carnegie Mellon University（卡内基梅隆大学）；Harbin Institute of Technology（哈尔滨工业大学）
RAVEN: Reinforcement-Adaptive Visibility-Graph Planning for Robust Humanoid Navigation with Collision-Free MPC
Robotics and Mechanisms Laboratory (RoMeLa), Department of Mechanical and Aerospace Engineering, University of California, Los Angeles（机器人与机构实验室（RoMeLa），机械与航天工程系，加利福尼亚大学洛杉矶分校）
DoublyAware: Dual Planning and Policy Awareness for Temporal Difference Learning in Humanoid Locomotion
University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Intelligent Autonomous Systems Lab（智能自主系统实验室）；German Research Center for AI (DFKI)（德国人工智能研究中心）；Automation & Control Institute (ACIN)（自动化与控制研究所）；Austrian Institute of Technology (AIT) GmbH（奥地利技术研究所）
Path and Motion Optimization for Efficient Multi-Location Inspection with Humanoid Robots
State Key Laboratory of General Artificial Intelligence, Beijing Institute for General Artificial Intelligence (BIGAI), Beijing（国家通用人工智能重点实验室，北京通用人工智能研究院（BIGAI））；Department of Automation, Tsinghua University（自动化系，清华大学）；Department of Automation, University of Science and Technology of China（自动化系，中国科学技术大学）；Department of Computer Science, Harbin Institute of Technology（计算机科学系，哈尔滨工业大学）；Institute for Artificial Intelligence and School of Artificial Intelligence, Peking University（人工智能研究院和人工智能学院，北京大学）
Humanoid robots are increasingly demanded to operate in interactive and human-surrounded environments while achieving sophisticated locomotion and manipulation tasks. To accomplish these tasks, roboticists unremittingly seek for advanced methods that generate whole-body coordination behaviors and meanwhile fulfill various planning and control objectives. Undoubtedly, these goals pose fundamental challenges to the robotics and control community. To take an incremental step towards reducing the performance gap between theoretical foundations and real implementations, we present a planning and co…
PolygMap: A Perceptive Locomotion Framework for Humanoid Robot Stair Climbing
The Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳人工智能与机器人社会研究院）；Electronic science and technology, Guangdong University of Technology（广东技术大学电子科学与技术学院）；Leju robotics（乐居机器人）
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Teleoperation has emerged as an alternative solution to fully-autonomous systems for achieving human-level capabilities on humanoids. Specifically, teleoperation with whole-body control is a promising hands-free strategy to command humanoids but demands more physical and mental effort. To mitigate this limitation, researchers have proposed shared-control methods incorporating robot decision-making to aid humans on low-level tasks, further reducing operation effort. However, shared-control methods for wheeled humanoid telelocomotion on a whole-body level has yet to be explored. In this work, we…
TD-GRPC: Temporal Difference Learning with Group Relative Policy Constraint for Humanoid Locomotion
University of Texas at Arlington（德克萨斯大学阿灵顿分校）；VinRobotics；Intelligent Autonomous Systems Lab, TU Darmstadt（图宾根大学智能自主系统实验室）；German Research Center for AI (DFKI)（德国人工智能研究中心）；Automation & Control Institute (ACIN), TU Wien（维也纳技术大学自动化与控制研究所）
South China University of Technology（华南理工大学）；Nanyang Technological University（南洋理工大学）；Georgia Institute of Technology（佐治亚理工学院）
The gesture-determined-dynamic function (GDDF) offers an effective way to handle the control problems of humanoid robots. Specifically, GDDF is utilized to constrain the movements of dual arms of humanoid robots and steer specific gestures to conduct demanding tasks under certain conditions. However, there is still a deficiency in this scheme. Through experiments, we found that the joints of the dual arms, which can be regarded as the redundant manipulators, could exceed their limits slightly at the joint angle level. The performance straightly depends on the parameters designed beforehand for…
This paper presents Latent Sampling-based Motion Planning (L-SBMP), a methodology towards computing motion plans for complex robotic systems by learning a plannable latent representation. Recent works in control of robotic systems have effectively leveraged local, low-dimensional embeddings of high-dimensional dynamics. In this paper we combine these recent advances with techniques from sampling-based motion planning (SBMP) in order to design a methodology capable of planning for high-dimensional robotic systems beyond the reach of traditional approaches (e.g., humanoids, or even systems where…
Osaka University（大阪大学）；National Inst. of AIST（产业技术综合研究所）
This paper presents a Center of Mass (CoM) based manipulation and regrasp planner that implements stability constraints to preserve the robot balance. The planner provides a graph of IK-feasible, collision-free and stable motion sequences, constructed using an energy based motion planning algorithm. It assures that the assembly motions are stable and prevent the robot from falling while performing dexterous tasks in different situations. Furthermore, the constraints are also used to perform an RRT-inspired task-related stability estimation in several simulations. The estimation can be used to …
International Institute of Information Technology, Hyderabad（海得拉巴国际信息技术研究所）
With the advent of artificial intelligence and machine learning, humanoid robots are made to learn a variety of skills which humans possess. One of fundamental skills which humans use in day-to-day activities is performing tasks with coordination between both the hands. In case of humanoids, learning such skills require optimal motion planning which includes avoiding collisions with the surroundings. In this paper, we propose a framework to learn coordinated tasks in cluttered environments based on DiGrad - A multi-task reinforcement learning algorithm for continuous action-spaces. Further, we…
University of Colorado（科罗拉多大学）；Google [x]；Rice University（莱斯大学）
We present an experienced-based planning framework called Thunder that learns to reduce computation time required to solve high-dimensional planning problems in varying environments. The approach is especially suited for large configuration spaces that include many invariant constraints, such as those found with whole body humanoid motion planning. Experiences are generated using probabilistic sampling and stored in a sparse roadmap spanner (SPARS), which provides asymptotically near-optimal coverage of the configuration space, making storing, retrieving, and repairing past experiences very ef…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Carnegie Mellon University（卡内基梅隆大学）
Bilateral teleoperation provides humanoid robots with human planning intelligence while enabling the human to feel what the robot feels. It has the potential to transform physically capable humanoid robots into dynamically intelligent ones. However, dynamic bilateral locomotion teleoperation remains as a challenge due to the complex dynamics it involves. This work presents our initial step to tackle this challenge via the concept of wheeled humanoid robot locomotion teleoperation by body tilt. Specifically, we developed a force-feedback-capable whole-body human-machine interface (HMI), and des…
The kinematic features of a centaur-type humanoid platform, combined with a powerful actuation, enable the experimentation of a variety of agile and dynamic motions. However, the higher number of degrees-of-freedom and the increased weight of the system, compared to the bipedal and quadrupedal counterparts, pose significant research challenges in terms of computational load and real implementation. To this end, this work presents a control architecture to perform agile actions, conceived for torque-controlled platforms, which decouples for computational purposes offline optimal control plannin…
PR2: A Physics- and Photo-realistic Humanoid Testbed with Pilot Study in Competition
State Key Laboratory of General Artificial Intelligence（一般人工智能国家重点实验室）；Beijing Institute for General Artificial Intelligence (BIGAI)（一般人工智能北京研究院）；School of Future Science and Engineering（未来科学与工程学院）；Soochow University（苏州大学）；Department of Computer Science（计算机科学系）；Harbin Institute of Technology（哈尔滨工业大学）；School of Mechatronics Engineering（机械电子工程学院）
University of South Carolina（南卡罗来纳大学）；United States Naval Academy（美国海军学院）
Despite the performance advantages of modern sampling-based motion planners, solving high dimensional planning problems in near real-time remains a challenge. Applications include hyper-redundant manipulators, snake-like and humanoid robots. Based on the intuition that many of these problem instances do not require the robots to exercise every degree of freedom independently, we introduce an enhancement to popular sampling-based planning algorithms aimed at circumventing the exponential dependence on dimensionality. We propose beginning the search in a lower dimensional subspace of the configu…
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
Creating natural-looking running gaits for humanoid robots is a complex task due to the underactuated degree of freedom in the trunk, which makes the motion planning and control difficult. The research on trunk movements in human locomotion is insufficient, and no formalism is known to transfer human motion patterns onto robots. Related work mostly focuses on the lower extremities, and simplifies the problem by stabilizing the trunk at a fixed angle. In contrast, humans display significant trunk motions that follow the natural dynamics of the gait. In this work, we use a spring-loaded inverted…
We consider the problem of planning a collision-free path for a high-dimensional robot. Specifically, we suggest a planning framework where a motion-planning algorithm can obtain guidance from a user. In contrast to existing approaches that try to speed up planning by incorporating experiences or demonstrations ahead of planning, we suggest to seek user guidance only when the planner identifies that it ceases to make significant progress towards the goal. Guidance is provided in the form of an intermediate configuration $\hat{q}$, which is used to bias the planner to go through $\hat{q}$. We d…
Model Predictive Control with Visibility Graphs for Humanoid Path Planning and Tracking Against Adversarial Opponents
Robotics and Mechanisms Laboratory (RoMeLa), Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, CA 90095, USA（机器人与机制实验室（RoMeLa）、机械与航空航天工程系、加州大学洛杉矶分校）
Dynamic Whole-Body Dancing with Humanoid Robots -- A Model-Based Control Approach
University of Science and Technology of China（中国科学技术大学）；Tsinghua University（清华大学）；Beijing Institute for General Artificial Intelligence (BIGAI)（北京通用人工智能研究院）；Xidian University（西安电子科技大学）；Harbin Institute of Technology（哈尔滨工业大学）
I-BFM: Reward-Conditioned Robust Humanoid Interaction via Unsupervised Reinforcement Learning
Tongji University（同济大学）；Tsinghua University（清华大学）；Zhejiang University（浙江大学）；RoboParty Lab（RoboParty实验室）；Harbin Institute of Technology（哈尔滨工业大学）；ShanghaiTech University（上海科技大学）
