9 citations · 9 across the 4 of their papers we have counts for
5 papers
EmbodiedSWE: Coding Agents for Long Horizon Dexterous Robotics
Haoxiang You, Zeyu Shen, Yilang Liu +16
We study coding agents for long-horizon, dexterous robotics and ask whether their solutions can provide scalable supervision for learning general robot policies. To test this, we d…
HIGenNTO: Scalable Humanoid Interaction Generation via Noise-Space Trajectory Optimization
Lalit Jayanti, Kashu Yamazaki, Yuto Shibata +2
Humanoid robots can acquire complex skills by imitating kinematic humanoid motion references, yet reliable references for contact-rich interactions remain difficult to obtain: moti…
Tactile Genesis: Exploring Tactile Sensors at Scale for Learning Dexterous Tasks
Trinity Chung, Kashu Yamazaki, Dhruv Patel +4
Tactile sensing is critical for contact-rich dexterous manipulation, yet it remains unclear which tactile abstractions a policy needs and when richer tactile fields justify their h…
Learning to Assist: Physics-Grounded Human-Human Control via Multi-Agent Reinforcement Learning
Yuto Shibata, Kashu Yamazaki, Lalit Jayanti +3
Humanoid robotics has strong potential to transform daily service and caregiving applications. Although recent advances in general motion tracking within physics engines (GMT) have…
Planning with Spatial-Temporal Abstraction from Point Clouds for Deformable Object Manipulation
Xingyu Lin, Carl Qi, Yunchu Zhang +5
Effective planning of long-horizon deformable object manipulation requires suitable abstractions at both the spatial and temporal levels. Previous methods typically either focus on…