We present a model predictive controller (MPC) that automatically discovers collision-free locomotion while simultaneously taking into account the system dynamics, friction constraints, and kinematic limitations. A relaxed barrier function is added to the optimization's cost function, leading to collision avoidance behavior without increasing the problem's computational complexity. Our holistic approach does not require any heuristics and enables legged robots to find whole-body motions in the presence of static and dynamic obstacles. We use a dynamically generated euclidean signed distance field for static collision checking. Collision checking for dynamic obstacles is modeled with moving cylinders, increasing the responsiveness to fast-moving agents. Furthermore, we include a Kalman filter motion prediction for moving obstacles into our receding horizon planning, enabling the robot to anticipate possible future collisions. Our experiments demonstrate collision-free motions on a quadrupedal robot in challenging indoor environments. The robot handles complex scenes like overhanging obstacles and dynamic agents by exploring motions at the robot's dynamic and kinematic limits.
Our paper proposes a model predictive controller as a single-task formulation that simultaneously optimizes wheel and torso motions. This online joint velocity and ground reaction force optimization integrates a kinodynamic model of a wheeled quadrupedal robot. It defines the single rigid body dynamics along with the robot's kinematics while treating the wheels as moving ground contacts. With this approach, we can accurately capture the robot's rolling constraint and dynamics, enabling automatic discovery of hybrid maneuvers without needless motion heuristics. The formulation's generality thro…
Adaptive Non-linear Centroidal MPC with Stability Guarantees for Robust Locomotion of Legged Robots
Artificial and Mechanical Intelligence (AMI), Istituto Italiano di Tecnologia (IIT)（人工智能与机械智能（AMI），意大利技术研究院（IIT））；Dynamic Legged Systems (DLS), Istituto Italiano di Tecnologia (IIT)（动态腿部系统（DLS），意大利技术研究院（IIT））；Frontier Robotics, Innovative Research Excellence（前沿机器人，创新研究卓越；本田研发，日本埼玉）；Honda R&D, Saitama, Japan（机器学习与优化，曼彻斯特大学）；Machine Learning and Optimisation, The University of Manchester
This work develops a learning-based contact estimator for legged robots that bypasses the need for physical sensors and takes multi-modal proprioceptive sensory data as input. Unlike vision-based state estimators, proprioceptive state estimators are agnostic to perceptually degraded situations such as dark or foggy scenes. While some robots are equipped with dedicated physical sensors to detect necessary contact data for state estimation, some robots do not have dedicated contact sensors, and the addition of such sensors is non-trivial without redesigning the hardware. The trained network can …
University of Michigan（密歇根大学）；Cornell University（康奈尔大学）
This paper reports on a new error-state Model Predictive Control (MPC) approach to connected matrix Lie groups for robot control. The linearized tracking error dynamics and the linearized equations of motion are derived in the Lie algebra. Moreover, given an initial condition, the linearized tracking error dynamics and equations of motion are globally valid and evolve independently of the system trajectory. By exploiting the symmetry of the problem, the proposed approach shows faster convergence of rotation and position simultaneously than the state-of-the-art geometric variational MPC based o…
Istituto Italiano di Tecnologia（意大利理工学院）；University of Notre Dame（圣母大学）
We present a novel control strategy for dynamic legged locomotion in complex scenarios, that considers information about the morphology of the terrain in contexts when only on-board mapping and computation are available. The strategy is built on top of two main elements: first a contact sequence task that provides safe foothold locations based on a convolutional neural network to perform fast and continuous evaluation of the terrain in search of safe foothold locations; then a model predictive controller that considers the foothold locations given by the contact sequence task to optimize targe…
Whole-Body Model-Predictive Control of Legged Robots with MuJoCo
Department Aeronautics and Astronautics, Massachusetts Institute of Technology（航空宇航与工程力学系，麻省理工学院）；Google DeepMind（谷歌DeepMind）；Department of Electrical and Computer Engineering, Carnegie Mellon University（电气与计算机工程系，卡内基梅隆大学）；Robotics Institute, Carnegie Mellon University（机器人研究所，卡内基梅隆大学）
Department of Mechanical Engineering and Materials Science, Duke University（机械工程与材料科学系，杜克大学）；Department of Electrical and Computer Engineering, Duke University（电气与计算机工程系，杜克大学）；Department of Computer Science, Duke University（计算机科学系，杜克大学）
Whole-Body Inverse Dynamics MPC for Legged Loco-Manipulation
Department of Mechanical and Process Engineering, ETH Zurich（机械与过程工程系，苏黎世联邦理工学院）；Computational Robotics Lab, Department of Computer Science, ETH Zurich（计算机器人实验室，计算机科学系，苏黎世联邦理工学院）
Adaptive-MHE : A Sampling-Based Adaptive MPC for Legged Loco-Manipulation via Moving Horizon Estimation
University of Calgary（卡尔加里大学）；Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich (TUM)（慕尼黑工业大学机器人与机器智能研究所（MIRMI））
Robots with Attitude: Singularity-Free Quaternion-Based Model-Predictive Control for Agile Legged Robots
Center for Robotics and Biosystems, Northwestern University（西北大学机器人与生物系统中心）；Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）；Department of Mechanical Engineering, Carnegie Mellon University（卡内基梅隆大学机械工程系）
Adaptive Legged Locomotion via Online Learning for Model Predictive Control
Department of Aerospace Engineering, University of Michigan（密歇根大学航空航天工程系）；Institute for Robotics and Intelligent Machines, Georgia Institute of Technology（佐治亚理工学院机器人与智能机器研究所）
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 …
Primal-Dual iLQR for GPU-Accelerated Learning and Control in Legged Robots
Dynamic Legged Systems Laboratory, Istituto Italiano di Tecnologia (IIT), Genova, Italy（动态腿系统实验室，意大利技术研究院（IIT），意大利热那亚）；Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei Sistemi (DIBRIS), Università di Genova, Genova, Italy（信息学院、生物工程、机器人与系统工程系（DIBRIS），热那亚大学，意大利热那亚）；Polytechnique Montréal, Montreal, Canada（蒙特利尔理工学院，加拿大蒙特利尔）
The feet of robots are typically used to design locomotion strategies, such as balancing, walking, and running. However, they also have great potential to perform manipulation tasks. In this paper, we propose a model predictive control (MPC) framework for a quadrupedal robot to dynamically balance on a ball and simultaneously manipulate it to follow various trajectories such as straight lines, sinusoids, circles and in-place turning. We numerically validate our controller on the Mini Cheetah robot using different gaits including trotting, bounding, and pronking on the ball.
This paper reports on developing an integrated framework for safety-aware informative motion planning suitable for legged robots. The information-gathering planner takes a dense stochastic map of the environment into account, while safety constraints are enforced via Control Barrier Functions (CBFs). The planner is based on the Incrementally-exploring Information Gathering (IIG) algorithm and allows closed-loop kinodynamic node expansion using a Model Predictive Control (MPC) formalism. Robotic exploration and information gathering problems are inherently path-dependent problems. That is, the …
Pixels-to-Graph: Real-time Integration of Building Information Models and Scene Graphs for Semantic-Geometric Human-Robot Understanding
NASA Jet Propulsion Laboratory, California Institute of Technology（美国国家航空航天局喷气推进实验室、加州理工学院）；Department of Electrical And Information Engineering, Polytechnic University of Bari（巴里理工学院电子与信息工程系）；Field AI
Istituto Italiano di Tecnologia (IIT)（意大利技术研究院）；Università di Genova（热那亚大学）；Università di Trento（特伦托大学）；IMT School for Advanced Studies Lucca（卢卡IMT高等研究院）
Model Predictive Control (MPC) approaches are widely used in robotics, since they guarantee feasibility and allow the computation of updated trajectories while the robot is moving. They generally require heuristic references for the tracking terms and proper tuning of the parameters of the cost function in order to obtain good performance. For instance, when a legged robot has to react to disturbances from the environment (e.g., recover after a push) or track a specific goal with statically unstable gaits, the effectiveness of the algorithm can degrade. In this work, we propose a novel optimiz…
LLM-Land: Large Language Models for Context-Aware Drone Landing
Department of Electrical and Computer Engineering（电气与计算机工程系）；Drexel University（德雷塞尔大学）；Department of Electrical and Systems Engineering（电气与系统工程系）；University of Pennsylvania（宾夕法尼亚大学）
Modern robotic systems are endowed with superior mobility and mechanical skills that make them suited to be employed in real-world scenarios, where interactions with heavy objects and precise manipulation capabilities are required. For instance, legged robots with high payload capacity can be used in disaster scenarios to remove dangerous material or carry injured people. It is thus essential to develop planning algorithms that can enable complex robots to perform motion and manipulation tasks accurately. In addition, online adaptation mechanisms with respect to new, unknown environments are n…
Performing highly agile dynamic motions, such as jumping or running on uneven stepping stones has remained a challenging problem in legged robot locomotion. This paper presents a framework that combines trajectory optimization and model predictive control to perform robust and consecutive jumping on stepping stones. In our approach, we first utilize trajectory optimization based on full-nonlinear dynamics of the robot to generate periodic jumping trajectories for various jumping distances. A jumping controller based on a model predictive control is then designed for realizing smooth jumping tr…
Multi-contact Stochastic Predictive Control for Legged Robots with Contact Locations Uncertainty
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Tandon School of Engineering, New York University（纽约大学坦登工程学院）；Munich Institute of Robotics and Machine Intelligence, Technical University of Munich（慕尼黑工业大学慕尼黑机器人与机器智能研究所）；University of Trento（特伦托大学）
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；University of Trento（特伦托大学）；K. N. Toosi University of Technology（K. N. 图西理工大学）；New York University（纽约大学）
Model predictive control (MPC) has shown great success for controlling complex systems such as legged robots. However, when closing the loop, the performance and feasibility of the finite horizon optimal control problem (OCP) solved at each control cycle is not guaranteed anymore. This is due to model discrepancies, the effect of low-level controllers, uncertainties and sensor noise. To address these issues, we propose a modified version of a standard MPC approach used in legged locomotion with viability (weak forward invariance) guarantees. In this approach, instead of adding a (conservative)…
