Dynamic locomotion in rough terrain requires accurate foot placement, collision avoidance, and planning of the underactuated dynamics of the system. Reliably optimizing for such motions and interactions in the presence of imperfect and often incomplete perceptive information is challenging. We present a complete perception, planning, and control pipeline, that can optimize motions for all degrees of freedom of the robot in real-time. To mitigate the numerical challenges posed by the terrain a sequence of convex inequality constraints is extracted as local approximations of foothold feasibility and embedded into an online model predictive controller. Steppability classification, plane segmentation, and a signed distance field are precomputed per elevation map to minimize the computational effort during the optimization. A combination of multiple-shooting, real-time iteration, and a filter-based line-search are used to solve the formulated problem reliably and at high rate. We validate the proposed method in scenarios with gaps, slopes, and stepping stones in simulation and experimentally on the ANYmal quadruped platform, resulting in state-of-the-art dynamic climbing.
As both legged robots and embedded compute have become more capable, researchers have started to focus on field deployment of these robots. Robust autonomy in unstructured environments requires perception of the world around the robot in order to avoid hazards. However, incorporating perception online while maintaining agile motion is more challenging for legged robots than other mobile robots due to the complex planners and controllers required to handle the dynamics of locomotion. This report will compare three recent approaches for perceptive locomotion and discuss the different ways in whi…
Institute for Infocomm Research, A*STAR（新加坡科技研究局资讯通信研究院）；University College London（伦敦大学学院）
This work developed a learning framework for perceptive legged locomotion that combines visual feedback, proprioceptive information, and active gait regulation of foot-ground contacts. The perception requires only one forward-facing camera to obtain the heightmap, and the active regulation of gait paces and traveling velocity are realized through our formulation of CPG-based high-level imitation of foot-ground contacts. Through this framework, an end-user has the ability to command task-level inputs to control different walking speeds and gait frequencies according to the traversal of differen…
Oxford Robotics Institute, University of Oxford（牛津大学牛津机器人研究所）
To dynamically traverse challenging terrain, legged robots need to continually perceive and reason about upcoming features, adjust the locations and timings of future footfalls and leverage momentum strategically. We present a pipeline that enables flexibly-parametrized trajectories for perceptive and dynamic quadruped locomotion to be optimized in an online, receding-horizon manner. The initial guess passed to the optimizer affects the computation needed to achieve convergence and the quality of the solution. We consider two methods for generating good guesses. The first is a heuristic initia…
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）
Robotic Systems Lab, ETH Zurich（苏黎世联邦理工学院机器人系统实验室）；Robotics and Artificial Intelligence Lab, KAIST（韩国科学技术院机器人与人工智能实验室）；Intelligent Systems Lab, Intel（英特尔智能系统实验室）
Legged robots that can operate autonomously in remote and hazardous environments will greatly increase opportunities for exploration into under-explored areas. Exteroceptive perception is crucial for fast and energy-efficient locomotion: perceiving the terrain before making contact with it enables planning and adaptation of the gait ahead of time to maintain speed and stability. However, utilizing exteroceptive perception robustly for locomotion has remained a grand challenge in robotics. Snow, vegetation, and water visually appear as obstacles on which the robot cannot step~-- or are missing …
Gait-Adaptive Perceptive Humanoid Locomotion with Real-Time Under-Base Terrain Reconstruction
Department of Electronic Engineering and Information Science (EEIS), University of Science and Technology of China（电子工程与信息科学系，中国科学技术大学）；LimX Dynamics（LimX动力学）；Hong Kong University of Science and Technology（香港科技大学）；School of Mechanics Engineering, Harbin Institute of Technology (HIT)（机械工程学院，哈尔滨工业大学）；Department of Computing, The Hong Kong Polytechnic University（计算学院，香港理工大学）；Zhejiang University-University of Illinois Urbana-Champaign Institute (ZJUI)（浙江大学-伊利诺伊大学厄巴纳-香槟分校联合研究所）
PILOT: A Perceptive Integrated Low-level Controller for Loco-manipulation over Unstructured Scenes
School of Automation and Intelligent Sensing（自动化与智能感知学院）；Shanghai Jiao Tong University（上海交通大学）；Global College（全球学院）；school of Computer Science（计算机科学学院）；Shanghai Key Laboratory of Navigation and Location Based Services（导航与位置基于服务上海市重点实验室）
PGTT: Phase-Guided Terrain Traversal for Perceptive Legged Locomotion
Laboratory of Automation and Robotics (LAR) in the Department of Electrical & Computer Engineering, University of Patras（自动化与机器人实验室（LAR）（电气与计算机工程系，帕特拉斯大学））；Archimedes/Athena RC, Greece（阿基米德/雅典娜RC，希腊）；Athena - Research and Innovation Center in Information, Communication and Knowledge Technologies, Xanthi, Greece（雅典娜信息、通信和知识技术研究中心（Xanthi，希腊））；Computational Intelligence Laboratory (CILab), Department of Mathematics, University of Patras（计算智能实验室（CILab）（数学系，帕特拉斯大学））
We present a versatile nonlinear model predictive control (NMPC) formulation for quadrupedal locomotion. Our formulation jointly optimizes a base trajectory and a set of footholds over a finite time horizon based on simplified dynamics models. We leverage second-order sensitivity analysis and a sparse Gauss-Newton (SGN) method to solve the resulting optimal control problems. We further describe our ongoing effort to verify our approach through simulation and hardware experiments. Finally, we extend our locomotion framework to deal with challenging tasks that comprise gap crossing, movement on …
Perceptive Behavior Foundation Model: Adapting Human Motion Priors to Robot-Centric Terrain
Mondo Robotics；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；Artificial General Intelligence Institute, University of Science and Technology of China（中国科学技术大学通用人工智能研究院）
IEETA / DETI University of Aveiro（阿威罗大学IEETA / DETI）
Developing robust locomotion for humanoid robots is a complex task due to the unstable nature of these robots and also to the unpredictability of the terrain. A robust locomotion planner is one of the fundamental components for generating stable biped locomotion. This paper presents an optimal closed-loop biped locomotion planner which can plan reference trajectories even in challenging conditions. The proposed planner is designed based on a Time-Varying Model Predictive Control~(TVMPC) scheme to be able to consider some constraints in the states, inputs and outputs of the system and also mixe…
Hybrid Data-Driven Predictive Control for Robust and Reactive Exoskeleton Locomotion Synthesis
Department of Computation and Neural Systems, Caltech（计算与神经系统系，加州理工学院）；Department of Mechanical and Civil Engineering, Caltech（机械与土木工程系，加州理工学院）；Wandercraft；Department of Computing and Mathematical Sciences, Caltech（计算与数学科学系，加州理工学院）；Department of Control and Dynamical Systems, Caltech（控制与动态系统系，加州理工学院）
Dynamic legged locomotion is a challenging topic because of the lack of established control schemes which can handle aerial phases, short stance times, and high-speed leg swings. In this paper, we propose a controller combining whole-body control (WBC) and model predictive control (MPC). In our framework, MPC finds an optimal reaction force profile over a longer time horizon with a simple model, and WBC computes joint torque, position, and velocity commands based on the reaction forces computed from MPC. Unlike existing WBCs, which attempt to track commanded body trajectories, our controller i…
Beijing Research Institute of UBTECH Robotics（优必选机器人北京研究院）；Tsinghua University（清华大学）
The planning of whole-body motion and step time for bipedal locomotion is constructed as a model predictive control (MPC) problem, in which a sequence of optimization problems needs to be solved online. While directly solving these problems is extremely time-consuming, we propose a predictive gait synthesizer to offer immediate solutions. Based on the full-dimensional model, a library of gaits with different speeds and periods is first constructed offline. Then the proposed gait synthesizer generates real-time gaits at 1kHz by synthesizing the gait library based on the online prediction of cen…
ETH Zürich（苏黎世联邦理工学院）；California Institute of Technology（加州理工学院）
The ability to generate dynamic walking in real-time for bipedal robots with input constraints and underactuation has the potential to enable locomotion in dynamic, complex and unstructured environments. Yet, the high-dimensional nature of bipedal robots has limited the use of full-order rigid body dynamics to gaits which are synthesized offline and then tracked online. In this work we develop an online nonlinear model predictive control approach that leverages the full-order dynamics to realize diverse walking behaviors. Additionally, this approach can be coupled with gaits synthesized offlin…
Physics-informed Neural Network Predictive Control for Quadruped Locomotion
School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区智能系统工程学院）；General Embodied AI Center of Sun Yat-sen University（中山大学通用具身人工智能中心）；Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology（广东省火灾科学与智能应急技术重点实验室）；College of Engineering, Peking University（北京大学工学院）；Robotics and Autonomous Systems Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）机器人与自主系统部）；George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology（佐治亚理工学院乔治·W. 伍德拉夫机械工程学院）
Convex model predictive controls (MPCs) with a single rigid body model have demonstrated strong performance on real legged robots. However, convex MPCs are limited by their assumptions such as small rotation angle and pre-defined gait, limiting the richness of potential solutions. We remove those assumptions and solve the complete mixed-integer non-convex programming with single rigid body model. We first collect datasets of pre-solved problems offline, then learn the problem-solution map to solve this optimization fast for MPC. If warm-starts can be found, offline problems can be solved close…
In this paper, previous works on the Model Predictive Control (MPC) and the Divergent Component of Motion (DCM) for bipedal walking control are extended. To this end, we employ a single MPC which uses a combination of Center of Pressure (CoP) manipulation, step adjustment, and Centroidal Moment Pivot (CMP) modulation to design a robust walking controller. Furthermore, we exploit the concept of time-varying DCM to generalize our walking controller for walking in uneven surfaces. Using our scheme, a general and robust walking controller is designed which can be implemented on robots with differe…
Optimal control is a successful approach to generate motions for complex robots, in particular for legged locomotion. However, these techniques are often too slow to run in real time for model predictive control or one needs to drastically simplify the dynamics model. In this work, we present a method to learn to predict the gradient and hessian of the problem value function, enabling fast resolution of the predictive control problem with a one-step quadratic program. In addition, our method is able to satisfy constraints like friction cones and unilateral constraints, which are important for …
Fondazione Istituto Italiano di Tecnologia（意大利技术研究院基金会）；Università di Genova（热那亚大学）
Humanoid robots are machines built with an anthropomorphic shape. Despite decades of research into the subject, it is still challenging to tackle the robot locomotion problem from an algorithmic point of view. For example, these machines cannot achieve a constant forward body movement without exploiting contacts with the environment. The reactive forces resulting from the contacts are subject to strong limitations, complicating the design of control laws. As a consequence, the generation of humanoid motions requires to exploit fully the mathematical model of the robot in contact with the envir…
Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier (LIRMM), CNRS–University of Montpellier（蒙彼利埃计算机科学、机器人技术与微电子实验室（LIRMM），法国国家科学研究中心–蒙彼利埃大学）；School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与航空航天工程学院）
We present a model predictive controller (MPC) for multi-contact locomotion where predictive optimizations are realized by time-optimal path parameterization (TOPP). A key feature of this solution is that, contrary to existing planners where step timings are provided as inputs, here the timing between contact switches is computed as output of a fast nonlinear optimization. This is particularly appealing to multi-contact locomotion, where proper timings depend on terrain topology and suitable heuristics are unknown. We show how to formulate legged locomotion as a TOPP problem and demonstrate th…
University of Notre Dame（圣母大学）；Robotics at Google（谷歌机器人部门）
Reinforcement Learning (RL) has witnessed great strides for quadruped locomotion, with continued progress in the reliable sim-to-real transfer of policies. However, it remains a challenge to reuse a policy on another robot, which could save time for retraining. In this work, we present a framework for zero-shot policy retargeting wherein diverse motor skills can be transferred between robots of different shapes and sizes. The new framework centers on a planning-and-control pipeline that systematically integrates RL and Model Predictive Control (MPC). The planning stage employs RL to generate a…
LIPM-Guided Reinforcement Learning for Stable and Perceptive Locomotion in Bipedal Robots
School of Automation and Intelligent Manufacturing (AIM), Southern University of Science and Technology（自动化与智能制造学院，南方科技大学）；Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology（土木与环境工程系，香港科技大学）；Department of Mechanical Engineering, The University of Hong Kong（机械工程系，香港大学）；Zhejiang University-University of Illinois Urbana-Champaign Institute (ZJUI), Zhejiang University（浙江大学-伊利诺伊大学厄巴纳-香槟分校联合研究所，浙江大学）；LimX Dynamics, Shenzhen, China（深圳LimX动力有限公司）
Nonlinear Model Predictive Control for Robust Bipedal Locomotion: Exploring Angular Momentum and CoM Height Changes
School of Power and Mechanical Engineering, Wuhan University（武汉大学动力与机械学院）；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳人工智能与机器人研究院）；School of Mechanical Engineering, University of Leeds（利兹大学机械工程学院）；School of Informatics, The University of Edinburgh（爱丁堡大学信息学院）；Humanoid and Human Centered Mechatronics Research Line, Istituto Italiano di Tecnologia（意大利技术研究院人形与以人为中心的机电一体化研究线）
Shield-Loco: Shielding Locomotion Policies with Predictive Safety Filtering
Robert Bosch Center for Cyber Physical Systems（罗伯特·博世网络物理系统中心）；Indian Institute of Science（印度科学研究院）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所（MIRMI））；Technical University of Munich（慕尼黑技术大学）；Department of Computer Science & Automation（计算机科学与自动化部门）；Department of Mechanical Engineering（机械工程系）；Carnegie Mellon University（卡内基梅隆大学）；Institute for Advanced Study（高级研究 institute）
ETH Zürich（苏黎世联邦理工学院）；California Institute of Technology（加州理工学院）
The problem of dynamic locomotion over rough terrain requires both accurate foot placement together with an emphasis on dynamic stability. Existing approaches to this problem prioritize immediate safe foot placement over longer term dynamic stability considerations, or relegate the coordination of foot placement and dynamic stability to heuristic methods. We propose a multi-layered locomotion framework that unifies Control Barrier Functions (CBFs) with Model Predictive Control (MPC) to simultaneously achieve safe foot placement and dynamic stability. Our approach incorporates CBF based safety …
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Vanderbilt University（范德堡大学）
We tackle the problem of perceptive locomotion in dynamic environments. In this problem, a quadrupedal robot must exhibit robust and agile walking behaviors in response to environmental clutter and moving obstacles. We present a hierarchical learning framework, named PRELUDE, which decomposes the problem of perceptive locomotion into high-level decision-making to predict navigation commands and low-level gait generation to realize the target commands. In this framework, we train the high-level navigation controller with imitation learning on human demonstrations collected on a steerable cart a…
