103 citations · 484 across the 48 of their papers we have counts for
8 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…
Vision Language Models are In-Context Value Learners
Yecheng Jason Ma, Joey Hejna, Ayzaan Wahid +15
Predicting temporal progress from visual trajectories is important for intelligent robots that can learn, adapt, and improve. However, learning such progress estimator, or temporal…
STEER: Flexible Robotic Manipulation via Dense Language Grounding
Laura Smith, Alex Irpan, Montserrat Gonzalez Arenas +4
The complexity of the real world demands robotic systems that can intelligently adapt to unseen situations. We present STEER, a robot learning framework that bridges high-level, co…
LeLaN: Learning A Language-Conditioned Navigation Policy from In-the-Wild Videos
Noriaki Hirose, Catherine Glossop, Ajay Sridhar +3
The world is filled with a wide variety of objects. For robots to be useful, they need the ability to find arbitrary objects described by people. In this paper, we present LeLaN(Le…
Gen2Act: Human Video Generation in Novel Scenarios enables Generalizable Robot Manipulation
Homanga Bharadhwaj, Debidatta Dwibedi, Abhinav Gupta +7
How can robot manipulation policies generalize to novel tasks involving unseen object types and new motions? In this paper, we provide a solution in terms of predicting motion info…
Mobility VLA: Multimodal Instruction Navigation with Long-Context VLMs and Topological Graphs
Hao-Tien Lewis Chiang, Zhuo Xu, Zipeng Fu +19
An elusive goal in navigation research is to build an intelligent agent that can understand multimodal instructions including natural language and image, and perform useful navigat…
SELFI: Autonomous Self-Improvement with Reinforcement Learning for Social Navigation
Noriaki Hirose, Dhruv Shah, Kyle Stachowicz +2
Autonomous self-improving robots that interact and improve with experience are key to the real-world deployment of robotic systems. In this paper, we propose an online learning met…
Pushing the Limits of Cross-Embodiment Learning for Manipulation and Navigation
Jonathan Yang, Catherine Glossop, Arjun Bhorkar +5
Recent years in robotics and imitation learning have shown remarkable progress in training large-scale foundation models by leveraging data across a multitude of embodiments. The s…