2 citations · 2 across the 4 of their papers we have counts for
5 papers · 1 filter
Traversability-Aware Legged Navigation by Learning from Real-World Visual Data
Hongbo Zhang, Zhongyu Li, Xuanqi Zeng +9
The enhanced mobility brought by legged locomotion empowers quadrupedal robots to navigate through complex and unstructured environments. However, optimizing agile locomotion while…
HiLMa-Res: A General Hierarchical Framework via Residual RL for Combining Quadrupedal Locomotion and Manipulation
Xiaoyu Huang, Qiayuan Liao, Yiming Ni +5
This work presents HiLMa-Res, a hierarchical framework leveraging reinforcement learning to tackle manipulation tasks while performing continuous locomotion using quadrupedal robot…
Commonsense Reasoning for Legged Robot Adaptation with Vision-Language Models
Annie S. Chen, Alec M. Lessing, Andy Tang +4
Legged robots are physically capable of navigating a diverse variety of environments and overcoming a wide range of obstructions. For example, in a search and rescue mission, a leg…
Adapt On-the-Go: Behavior Modulation for Single-Life Robot Deployment
Annie S. Chen, Govind Chada, Laura Smith +4
To succeed in the real world, robots must cope with situations that differ from those seen during training. We study the problem of adapting on-the-fly to such novel scenarios duri…
Grow Your Limits: Continuous Improvement with Real-World RL for Robotic Locomotion
Laura Smith, Yunhao Cao, Sergey Levine
Deep reinforcement learning (RL) can enable robots to autonomously acquire complex behaviors, such as legged locomotion. However, RL in the real world is complicated by constraints…