Although bipedal locomotion provides the ability to traverse unstructured environments, it requires careful planning and control to safely walk across without falling. This poses an integrated challenge for the robot to perceive, plan, and control its movements, especially with dynamic motions where the robot may have to adapt its swing-leg trajectory onthe-fly in order to safely place its foot on the uneven terrain. In this paper we present an efficient geometric footstep planner and the corresponding walking controller that enables a humanoid robot to dynamically walk across uneven terrain at speeds up to 0.3 m/s. As dynamic locomotion, we refer first to the continuous walking motion without stopping, and second to the on-the-fly replanning of the landing footstep position in middle of the swing phase during the robot gait cycle. This is mainly achieved through the streamlined integration between an efficient sampling-based planner and robust walking controller. The footstep planner is able to generate feasible footsteps within 5 milliseconds, and the controller is able to generate a new corresponding swing leg trajectory as well as the wholebody motion to dynamically balance the robot to the newly updated footsteps. The proposed perception-control pipeline is evaluated and demonstrated with real experiments using a fullscale humanoid to traverse across uneven terrains featured by static stepping stones, dynamically movable stepping stone, or narrow path.
Robust Humanoid Walking on Compliant and Uneven Terrain with Deep Reinforcement Learning
CNRS-AIST JRL (Joint Robotics Laboratory) IRL, National Institute of Advanced Industrial Science and Technology (AIST), Japan（日本国家先进工业科学与技术研究院（AIST））；University of Tsukuba（筑波大学）；Department of Computer Science, Stanford University（斯坦福大学计算机科学系）
This paper proposes a novel orientation-aware model predictive control (MPC) for dynamic humanoid walking that can plan footstep locations online. Instead of a point-mass model, this work uses the augmented single rigid body model (aSRBM) to enable the MPC to leverage orientation dynamics and stepping strategy within a unified optimization framework. With the footstep location as part of the decision variables in the aSRBM, the MPC can reason about stepping within the kinematic constraints. A task-space controller (TSC) tracks the body pose and swing leg references output from the MPC, while e…
NASA Johnson Space Center（NASA约翰逊航天中心）；TRACLabs；Jacobs Technology（雅各布斯技术公司）；METECS；CACI；The University of Texas at Austin（德克萨斯大学奥斯汀分校）
As part of a feasibility study, this paper shows the NASA Valkyrie humanoid robot performing an end-to-end improvised explosive device (IED) response task. To demonstrate and evaluate robot capabilities, sub-tasks highlight different locomotion, manipulation, and perception requirements: traversing uneven terrain, passing through a narrow passageway, opening a car door, retrieving a suspected IED, and securing the IED in a total containment vessel (TCV). For each sub-task, a description of the technical approach and the hidden challenges that were overcome during development are presented. The…
K. N. Toosi University of Technology（K. N. 图西理工大学）；University of Tehran（德黑兰大学）
In this paper, an online adaptation algorithm for bipedal walking on uneven surfaces with height uncertainty is proposed. In order to generate walking patterns on flat terrains, the trajectories in the task space are planned to satisfy the dynamic balance and slippage avoidance constraints, and also to guarantee smooth landing of the swing foot. To ensure smooth landing of the swing foot on surfaces with height uncertainty, the preplanned trajectories in the task space should be adapted. The proposed adaptation algorithm consists of two stages. In the first stage, once the swing foot reaches i…
Robot footstep planning strategies can be divided in two main approaches: discrete searches and continuous optimizations. While discrete searches have been broadly applied, continuous optimizations approaches have been restricted for humanoid platforms. This article introduces a generalized continuous-optimization approach for multilegged footstep planning which can be adapted to different platforms, regardless the number and geometry of legs. This approach leverages Mixed-Integer Convex Programming to account for the non-convex constraints that represent footstep rotation and obstacle avoidan…
The University of Edinburgh（爱丁堡大学）；Ewha Womans University（梨花女子大学）；LAAS-CNRS（法国国家科学研究中心分析与系统结构实验室）；Université de Toulouse（图卢兹大学）；University of Trento（特伦托大学）
One of the main challenges of planning legged locomotion in complex environments is the combinatorial contact selection problem. Recent contributions propose to use integer variables to represent which contact surface is selected, and then to rely on modern mixed-integer (MI) optimization solvers to handle this combinatorial issue. To reduce the computational cost of MI, we exploit the sparsity properties of L1 norm minimization techniques to relax the contact planning problem into a feasibility linear program. Our approach accounts for kinematic reachability of the center of mass (COM) and of…
VividFace: Real-Time and Realistic Facial Expression Shadowing for Humanoid Robots
School of Computing, Macquarie University（麦考瑞大学计算机学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算机与数据科学学院）；Anuradha and Vikas Sinha Department of Data Science, University of North Texas（北卡罗来纳州立大学数据科学系）
Georgia Institute of Technology（佐治亚理工学院）；Twentieth Century Fox（二十世纪福克斯公司）；Starfleet Academy（星际舰队学院）；Tyrell Inc.（泰伦公司）
This paper presents a multi-contact motion adaptation framework that enables teleoperation of high degree-of-freedom (DoF) robots, such as quadrupeds and humanoids, for loco-manipulation tasks in multi-contact settings. Our proposed algorithms optimize whole-body configurations and formulate the retargeting of multi-contact motions as sequential quadratic programming, which is robust and stable near the edges of feasibility constraints. Our framework allows real-time operation of the robot and reduces cognitive load for the operator because infeasible commands are automatically adapted into ph…
Learning Bipedal Walking for Humanoids with Current Feedback
CNRS-AIST JRL (Joint Robotics Laboratory) IRL（法国国家科学研究中心-产业技术综合研究所联合机器人实验室国际联合实验室）；National Institute of Advanced Industrial Science and Technology (AIST)（产业技术综合研究所）；University of Tsukuba（筑波大学）；Stanford University（斯坦福大学）
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；University of Southern California（南加州大学）
Linear models for control and motion generation of humanoid robots have received significant attention in the past years, not only due to their well known theoretical guarantees, but also because of practical computational advantages. However, to tackle more challenging tasks and scenarios such as locomotion on uneven terrain, a more expressive model is required. In this paper, we are interested in contact interaction-centered motion optimization based on the momentum dynamics model. This model is non-linear and non-convex; however, we find a relaxation of the problem that allows us to formula…
Wireless Center of Pressure Feedback System for Humanoid Robot Balance Control using ESP32-C3
Department of Computer Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia（印度尼西亚十月份十日理工大学计算机工程系）；Graduate School of Science and Technology, Kumamoto University, Japan（日本熊本大学研究生院）；The Science and Technology Center of Artificial Intelligence for Healthcare and Society (PUI AIHeS), Indonesia（印度尼西亚人工智能医疗与社会科技中心）；Department of Electrical Automation Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia（印度尼西亚十月份十日理工大学电气自动化工程系）；Department of Computing, Imperial College London, United Kingdom（英国伦敦帝国学院计算系）
