23 citations · 55 across the 17 of their papers we have counts for
21 papers
Hierarchical Residual Policy Learning for Real-World Mobile Manipulation with Sparse Human Guidance
Zhefei Gong, Hefeifei Jiang, Zehao Wang +1
Mobile Manipulation (MoMa) is a cornerstone of autonomous robotic systems, yet real-world fine-tuning remains a formidable challenge because of high-dimensional action spaces and i…
ResSafe: Learning Safety Filtering with Residual Reinforcement Learning for Humanoids
Gechen Qu, Tong Zhang, Bike Zhang +4
Safe control of humanoid robots remains challenging due to their high-dimensional dynamics, contact-rich interactions, and sensitivity to disturbances. Although reinforcement learn…
SPARK: Skeleton-Parameter Aligned Retargeting on Humanoid Robots with Kinodynamic Trajectory Optimization
Hanwen Wang, Qiayuan Liao, Bike Zhang +3
Human motion provides rich priors for training general-purpose humanoid control policies, but raw demonstrations are often incompatible with a robot's kinematics and dynamics, limi…
HITTER: A HumanoId Table TEnnis Robot via Hierarchical Planning and Learning
Zhi Su, Bike Zhang, Nima Rahmanian +5
Humanoid robots have recently achieved impressive progress in locomotion and whole-body control, yet they remain constrained in tasks that demand rapid interaction with dynamic env…
LangWBC: Language-directed Humanoid Whole-Body Control via End-to-end Learning
Yiyang Shao, Xiaoyu Huang, Bike Zhang +6
General-purpose humanoid robots are expected to interact intuitively with humans, enabling seamless integration into daily life. Natural language provides the most accessible mediu…
Berkeley Humanoid: A Research Platform for Learning-based Control
Qiayuan Liao, Bike Zhang, Xuanyu Huang +3
We introduce Berkeley Humanoid, a reliable and low-cost mid-scale humanoid research platform for learning-based control. Our lightweight, in-house-built robot is designed specifica…
Adaptive Energy Regularization for Autonomous Gait Transition and Energy-Efficient Quadruped Locomotion
Boyuan Liang, Lingfeng Sun, Xinghao Zhu +6
In reinforcement learning for legged robot locomotion, crafting effective reward strategies is crucial. Pre-defined gait patterns and complex reward systems are widely used to stab…
Humanoid Locomotion as Next Token Prediction
Ilija Radosavovic, Bike Zhang, Baifeng Shi +5
We cast real-world humanoid control as a next token prediction problem, akin to predicting the next word in language. Our model is a causal transformer trained via autoregressive p…
Constraint-Guided Online Data Selection for Scalable Data-Driven Safety Filters in Uncertain Robotic Systems
Jason J. Choi, Fernando Castañeda, Wonsuhk Jung +3
As the use of autonomous robots expands in tasks that are complex and challenging to model, the demand for robust data-driven control methods that can certify safety and stability…
Prompt a Robot to Walk with Large Language Models
Yen-Jen Wang, Bike Zhang, Jianyu Chen +1
Large language models (LLMs) pre-trained on vast internet-scale data have showcased remarkable capabilities across diverse domains. Recently, there has been escalating interest in…
Real-World Humanoid Locomotion with Reinforcement Learning
Ilija Radosavovic, Tete Xiao, Bike Zhang +3
Humanoid robots that can autonomously operate in diverse environments have the potential to help address labour shortages in factories, assist elderly at homes, and colonize new pl…
Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functions
Fernando Castañeda, Jason J. Choi, Wonsuhk Jung +3
Learning-based control has recently shown great efficacy in performing complex tasks for various applications. However, to deploy it in real systems, it is of vital importance to g…
Learning Torque Control for Quadrupedal Locomotion
Shuxiao Chen, Bike Zhang, Mark W. Mueller +2
Reinforcement learning (RL) has become a promising approach to developing controllers for quadrupedal robots. Conventionally, an RL design for locomotion follows a position-based p…
Learning Differentiable Safety-Critical Control using Control Barrier Functions for Generalization to Novel Environments
Hengbo Ma, Bike Zhang, Masayoshi Tomizuka +1
Control barrier functions (CBFs) have become a popular tool to enforce safety of a control system. CBFs are commonly utilized in a quadratic program formulation (CBF-QP) as safety-…
Pointwise Feasibility of Gaussian Process-based Safety-Critical Control under Model Uncertainty
Fernando Castañeda, Jason J. Choi, Bike Zhang +2
Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs) are popular tools for enforcing safety and stability of a controlled system, respectively. They are commonly…
Rule-Based Safety-Critical Control Design using Control Barrier Functions with Application to Autonomous Lane Change
Suiyi He, Jun Zeng, Bike Zhang +1
This paper develops a new control design for guaranteeing a vehicle's safety during lane change maneuvers in a complex traffic environment. The proposed method uses a finite state…
Safety-Critical Control using Optimal-decay Control Barrier Function with Guaranteed Point-wise Feasibility
Jun Zeng, Bike Zhang, Zhongyu Li +1
Safety is one of the fundamental problems in robotics. Recently, a quadratic program-based control barrier function (CBF) method has emerged as a way to enforce safety-critical con…
Gaussian Process-based Min-norm Stabilizing Controller for Control-Affine Systems with Uncertain Input Effects and Dynamics
Fernando Castañeda, Jason J. Choi, Bike Zhang +2
This paper presents a method to design a min-norm Control Lyapunov Function (CLF)-based stabilizing controller for a control-affine system with uncertain dynamics using Gaussian Pr…
Dynamic Legged Manipulation of a Ball Through Multi-Contact Optimization
Chenyu Yang, Bike Zhang, Jun Zeng +2
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 task…
Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function
Jun Zeng, Bike Zhang, Koushil Sreenath
The optimal performance of robotic systems is usually achieved near the limit of state and input bounds. Model predictive control (MPC) is a prevalent strategy to handle these oper…
Feedback Control for Autonomous Riding of Hovershoes by a Cassie Bipedal Robot
Shuxiao Chen, Jonathan Rogers, Bike Zhang +1
Motivated towards achieving multi-modal locomotion, in this paper, we develop a framework for a bipedal robot to dynamically ride a pair of Hovershoes over various terrain. Our dev…