10 citations · 23 across the 3 of their papers we have counts for
3 papers
cs.RO2024★ 6 cited
Scaling Cross-Embodied Learning: One Policy for Manipulation, Navigation, Locomotion and Aviation
Ria Doshi, Homer Walke, Oier Mees +2
Modern machine learning systems rely on large datasets to attain broad generalization, and this often poses a challenge in robot learning, where each robotic platform and task migh…
cs.RO2023★ 10 cited
Zero-Shot Robotic Manipulation with Pretrained Image-Editing Diffusion Models
Kevin Black, Mitsuhiko Nakamoto, Pranav Atreya +4
If generalist robots are to operate in truly unstructured environments, they need to be able to recognize and reason about novel objects and scenarios. Such objects and scenarios m…
cs.RO2022★ 7 cited
Don't Start From Scratch: Leveraging Prior Data to Automate Robotic Reinforcement Learning
Homer Walke, Jonathan Yang, Albert Yu +4
Reinforcement learning (RL) algorithms hold the promise of enabling autonomous skill acquisition for robotic systems. However, in practice, real-world robotic RL typically requires…