Reinforcement learning for versatile, dynamic, and robust bipedal locomotion control
This paper presents a comprehensive study on using deep reinforcement learning (RL) to
create dynamic locomotion controllers for bipedal robots. Going beyond focusing on a single …
create dynamic locomotion controllers for bipedal robots. Going beyond focusing on a single …
Lotus: Continual imitation learning for robot manipulation through unsupervised skill discovery
We introduce LOTUS, a continual imitation learning algorithm that empowers a physical
robot to continuously and efficiently learn to solve new manipulation tasks throughout its …
robot to continuously and efficiently learn to solve new manipulation tasks throughout its …
Equivariant diffusion policy
Recent work has shown diffusion models are an effective approach to learning the
multimodal distributions arising from demonstration data in behavior cloning. However, a …
multimodal distributions arising from demonstration data in behavior cloning. However, a …
FlightBench: A Comprehensive Benchmark of Spatial Planning Methods for Quadrotors
Spatial planning in cluttered environments is crucial for mobile systems, particularly agile
quadrotors. Existing methods, both optimization-based and learning-based, often focus only …
quadrotors. Existing methods, both optimization-based and learning-based, often focus only …
MATCH POLICY: A Simple Pipeline from Point Cloud Registration to Manipulation Policies
Many manipulation tasks require the robot to rearrange objects relative to one another. Such
tasks can be described as a sequence of relative poses between parts of a set of rigid …
tasks can be described as a sequence of relative poses between parts of a set of rigid …