Reinforcement learning for versatile, dynamic, and robust bipedal locomotion control

Z Li, XB Peng, P Abbeel, S Levine… - … Journal of Robotics …, 2024 - journals.sagepub.com
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 …

Lotus: Continual imitation learning for robot manipulation through unsupervised skill discovery

W Wan, Y Zhu, R Shah, Y Zhu - 2024 IEEE International …, 2024 - ieeexplore.ieee.org
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 …

Equivariant diffusion policy

D Wang, S Hart, D Surovik, T Kelestemur… - arXiv preprint arXiv …, 2024 - arxiv.org
Recent work has shown diffusion models are an effective approach to learning the
multimodal distributions arising from demonstration data in behavior cloning. However, a …

FlightBench: A Comprehensive Benchmark of Spatial Planning Methods for Quadrotors

SA Yu, C Yu, F Gao, Y Wu, Y Wang - arXiv preprint arXiv:2406.05687, 2024 - arxiv.org
Spatial planning in cluttered environments is crucial for mobile systems, particularly agile
quadrotors. Existing methods, both optimization-based and learning-based, often focus only …

MATCH POLICY: A Simple Pipeline from Point Cloud Registration to Manipulation Policies

H Huang, H Liu, D Wang, R Walters, R Platt - arXiv preprint arXiv …, 2024 - arxiv.org
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 …