7 citations · 16 across the 6 of their papers we have counts for
6 papers
Robust and Versatile Bipedal Jumping Control through Reinforcement Learning
Zhongyu Li, Xue Bin Peng, Pieter Abbeel +3
This work aims to push the limits of agility for bipedal robots by enabling a torque-controlled bipedal robot to perform robust and versatile dynamic jumps in the real world. We pr…
In-Distribution Barrier Functions: Self-Supervised Policy Filters that Avoid Out-of-Distribution States
Fernando Castañeda, Haruki Nishimura, Rowan McAllister +2
Learning-based control approaches have shown great promise in performing complex tasks directly from high-dimensional perception data for real robotic systems. Nonetheless, the lea…
Lyapunov Design for Robust and Efficient Robotic Reinforcement Learning
Tyler Westenbroek, Fernando Castaneda, Ayush Agrawal +2
Recent advances in the reinforcement learning (RL) literature have enabled roboticists to automatically train complex policies in simulated environments. However, due to the poor s…
Hierarchical Reinforcement Learning for Precise Soccer Shooting Skills using a Quadrupedal Robot
Yandong Ji, Zhongyu Li, Yinan Sun +4
We address the problem of enabling quadrupedal robots to perform precise shooting skills in the real world using reinforcement learning. Developing algorithms to enable a legged ro…
Collaborative Navigation and Manipulation of a Cable-towed Load by Multiple Quadrupedal Robots
Chenyu Yang, Guo Ning Sue, Zhongyu Li +6
This paper tackles the problem of robots collaboratively towing a load with cables to a specified goal location while avoiding collisions in real time. The introduction of cables (…
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-…