2 citations · 4 across the 8 of their papers we have counts for
8 papers
Flow-based Policy Adaptation without Policy Updates
Luzhe Sun, Jingtian Ji, Haoran Chen +2
Foundation models and other policies trained on large and diverse datasets can endow robots with semantic and task knowledge that would be difficult to acquire from task-specific d…
HapCompass: A Rotational Haptic Device for Contact-Rich Robotic Teleoperation
Xiangshan Tan, Jingtian Ji, Tianchong Jiang +2
The contact-rich nature of manipulation makes it a significant challenge for robotic teleoperation. While haptic feedback is critical for contact-rich tasks, providing intuitive di…
From Vague Instructions to Task Plans: A Feedback-Driven HRC Task Planning Framework based on LLMs
Afagh Mehri Shervedani, Matthew R. Walter, Milos Zefran
Recent advances in large language models (LLMs) have demonstrated their potential as planners in human-robot collaboration (HRC) scenarios, offering a promising alternative to trad…
Active Advantage-Aligned Online Reinforcement Learning with Offline Data
Xuefeng Liu, Hung T. C. Le, Siyu Chen +4
Online reinforcement learning (RL) enhances policies through direct interactions with the environment, but faces challenges related to sample efficiency. In contrast, offline RL le…
StackGen: Generating Stable Structures from Silhouettes via Diffusion
Luzhe Sun, Takuma Yoneda, Samuel W. Wheeler +2
Humans naturally obtain intuition about the interactions between and the stability of rigid objects by observing and interacting with the world. It is this intuition that governs t…
6-DoF Stability Field via Diffusion Models
Takuma Yoneda, Tianchong Jiang, Gregory Shakhnarovich +1
A core capability for robot manipulation is reasoning over where and how to stably place objects in cluttered environments. Traditionally, robots have relied on object-specific, ha…
Cold Diffusion on the Replay Buffer: Learning to Plan from Known Good States
Zidan Wang, Takeru Oba, Takuma Yoneda +3
Learning from demonstrations (LfD) has successfully trained robots to exhibit remarkable generalization capabilities. However, many powerful imitation techniques do not prioritize…
Blending Imitation and Reinforcement Learning for Robust Policy Improvement
Xuefeng Liu, Takuma Yoneda, Rick L. Stevens +2
While reinforcement learning (RL) has shown promising performance, its sample complexity continues to be a substantial hurdle, restricting its broader application across a variety…