23 citations · 55 across the 17 of their papers we have counts for
14 papers · 1 filter
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…
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…
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…
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…