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 limits are reflected in our kino-dynamic motion planner by approximating the configuration-dependent reaction force limits and in our dynamics simulator by including actuator dynamics along with the robot's full-body dynamics. For the landing control, we effectively integrate model-predictive control and whole-body impulse control by connecting them in a dynamically consistent way to accomplish both the long-time horizon optimal control and high-bandwidth full-body dynamics-based feedback. Actuators' torque output over the entire motion are validated based on the velocity-torque model including battery voltage droop and back-EMF voltage. With the carefully designed hardware and control framework, we successfully demonstrate dynamic behaviors such as back flips, front flips, and spinning jumps in our realistic dynamics simulation.
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 …
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…
From Sign Language Generation to Humanoid Execution: Vision-Language Guided Retargeting with Collision Mitigation
Department of Systems and Control Engineering, Institute of Science Tokyo（东京科学大学系统与控制工程系）；RIKEN Guardian Robot Project（理化学研究所守护机器人项目）；ATR Hiroshi Ishiguro Laboratories（ATR石黑浩实验室）；RIKEN Center for Biosystems Dynamics Research（理化学研究所生物系统动力学研究中心）
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…
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）；MIT Biomimetic Robotics Lab（麻省理工学院仿生机器人实验室）；NAVER LABS（NAVER实验室）
In this letter, we present a versatile hierarchical offline planning algorithm, along with an online control pipeline for agile quadrupedal locomotion. Our offline planner alternates between optimizing centroidal dynamics for a reduced-order model and whole-body trajectory optimization, with the aim of achieving dynamics consensus. Our novel momentum-inertia-aware centroidal optimization, which uses an equimomental ellipsoid parameterization, is able to generate highly acrobatic motions via ``inertia shaping". Our whole-body optimization approach significantly improves upon the quality of stan…
StyleLoco: Generative Adversarial Distillation for Natural Humanoid Robot Locomotion
National Key Laboratory of General Artificial Intelligence, BIGAI（通用人工智能国家重点实验室，BIGAI）；School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）；Huazhong University of Science and Technology（华中科技大学）
Mirror-Descent Inverse Kinematics for Box-constrained Joint Space
National Institute of Informatics (NII)（国立信息学研究所）；The Graduate University for Advanced Studies (SOKENDAI)（综合研究大学院大学（SOKENDAI））；Nara Institute of Science and Technology（奈良科学技术研究所）
Teacher Motion Priors: Enhancing Robot Locomotion over Challenging Terrain
University of Chinese Academy of Sciences（中国科学院大学）；Chinese Academy of Sciences（中国科学院）；Beijing Zhongke Huiling Robot Technology Co., LTD（北京中科创联机器人技术有限公司）
We present data from two online human-robot interaction experiments where 227 participants viewed videos of a humanoid robot exhibiting faulty or non-faulty behaviours while either remaining mute or speaking. The participants were asked to evaluate their perception of the robot's trustworthiness, as well as its likeability, animacy, and perceived intelligence. The results show that, while a non-faulty robot achieves the highest trust, an apparently faulty robot that can speak manages to almost completely mitigate the loss of trust that is otherwise seen with faulty behaviour. We theorize that …
In this letter, we formulate a novel Markov Decision Process (MDP) for safe and data-efficient learning for humanoid locomotion aided by a dynamic balancing model. In our previous studies of biped locomotion, we relied on a low-dimensional robot model, commonly used in high-level Walking Pattern Generators (WPGs). However, a low-level feedback controller cannot precisely track desired footstep locations due to the discrepancies between the full order model and the simplified model. In this study, we propose mitigating this problem by complementing a WPG with reinforcement learning. More specif…
This work combines control barrier functions (CBFs) with a whole-body controller to enable self-collision avoidance for the MIT Humanoid. Existing reactive controllers for self-collision avoidance cannot guarantee collision-free trajectories as they do not leverage the robot's full dynamics, thus compromising kinematic feasibility. In comparison, the proposed CBF-WBC controller can reason about the robot's underactuated dynamics in real-time to guarantee collision-free motions. The effectiveness of this approach is validated in simulation. First, a simple hand-reaching experiment shows that th…
Towards Bridging the Gap between Large-Scale Pretraining and Efficient Finetuning for Humanoid Control
State Key Laboratory of General Artificial Intelligence, BIGAI（通用人工智能国家重点实验室，BIGAI）；School of Artificial Intelligence, Xidian University（人工智能学院，西安电子科技大学）
SafeFall: Learning Protective Control for Humanoid Robots
School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）；National Key Laboratory of General Artificial Intelligence, BIGAI（通用人工智能国家重点实验室（BIGAI））；Department of Automation, Tsinghua University（清华大学自动化系）
OmniTrack: General Motion Tracking via Physics-Consistent Reference
Huazhong University of Science and Technology（华中科技大学）；State Key Lab of General AI, Beijing Institute for General Artificial Intelligence (BIGAI)（通用人工智能国家重点实验室，北京通用人工智能研究院）；Shanghai Jiao Tong University（上海交通大学）
Omnidirectional Humanoid Locomotion on Stairs via Unsafe Stepping Penalty and Sparse LiDAR Elevation Mapping
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院）；State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology（华中科技大学智能制造装备技术国家重点实验室）；School of Mechanical Science and Engineering, Huazhong University of Science and Technology（华中科技大学机械科学与工程学院）
QuietWalk: Physics-Informed Reinforcement Learning for Ground Reaction Force-Aware Humanoid Locomotion Under Diverse Footwear
Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences（宁波材料技术与工程研究所，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Zhejiang Key Laboratory of Precision Actuation and Intelligent Robotics（浙江精密作动与智能机器人重点实验室）；School of Engineering, Westlake University（西湖大学工程学院）
FastStair: Learning to Run Up Stairs with Humanoid Robots
School of Mechanics Engineering, Harbin Institute of Technology (HIT), Harbin Heilongjiang 150001, China（哈尔滨工业大学机械工程学院）；LimX Dynamics, Shenzhen, China（LimX Dynamics）；Zhejiang University-University of Illinois Urbana-Champaign Institute (ZJUI), Haining, China（浙江大学-伊利诺伊大学厄巴纳-香槟分校联合研究所）；Department of Electronic Engineering and Information Science (EEIS), University of Science and Technology of China, Hefei 230027, China（中国科学技术大学电子工程与信息科学系）；Hong Kong University of Science and Technology, Hong Kong SAR, China（香港科技大学）；Department of Mechanical Engineering, National University of Singapore, Singapore 117575（新加坡国立大学机械工程系）
University of Cagliari（卡利亚里大学）；Pluribus One（Pluribus One 公司）；University of Manchester（曼彻斯特大学）
Deep neural networks have been widely adopted in recent years, exhibiting impressive performances in several application domains. It has however been shown that they can be fooled by adversarial examples, i.e., images altered by a barely-perceivable adversarial noise, carefully crafted to mislead classification. In this work, we aim to evaluate the extent to which robot-vision systems embodying deep-learning algorithms are vulnerable to adversarial examples, and propose a computationally efficient countermeasure to mitigate this threat, based on rejecting classification of anomalous inputs. We…
A Three-Level Whole-Body Disturbance Rejection Control Framework for Dynamic Motions in Legged Robots
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（人工智能与自动化学院，华中科技大学）；State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology（智能制造装备与技术国家重点实验室，华中科技大学）
College of AI & School of Vehicle and Mobility, Tsinghua University（人工智能学院与车辆与移动系统学院，清华大学）；Berkeley AI Research (BAIR), UC Berkeley（伯克利人工智能研究（BAIR），加州大学伯克利分校）
