16 citations · 20 across the 7 of their papers we have counts for
7 papers
Heterogeneous Scientific Foundation Model Collaboration
Zihao Li, Jiaru Zou, Feihao Fang +6
Agentic large language model systems have demonstrated strong capabilities. However, their reliance on language as the universal interface fundamentally limits their applicability…
SICNav-Diffusion: Safe and Interactive Crowd Navigation with Diffusion Trajectory Predictions
Sepehr Samavi, Anthony Lem, Fumiaki Sato +5
To navigate crowds without collisions, robots must interact with humans by forecasting their future motion and reacting accordingly. While learning-based prediction models have sho…
One-Shot Transfer of Long-Horizon Extrinsic Manipulation Through Contact Retargeting
Albert Wu, Ruocheng Wang, Sirui Chen +2
Extrinsic manipulation, the use of environment contacts to achieve manipulation objectives, enables strategies that are otherwise impossible with a parallel jaw gripper. However, o…
Synthesize Dexterous Nonprehensile Pregrasp for Ungraspable Objects
Sirui Chen, Albert Wu, C. Karen Liu
Daily objects embedded in a contextual environment are often ungraspable initially. Whether it is a book sandwiched by other books on a fully packed bookshelf or a piece of paper l…
STAS: Spatial-Temporal Return Decomposition for Multi-agent Reinforcement Learning
Sirui Chen, Zhaowei Zhang, Yaodong Yang +1
Centralized Training with Decentralized Execution (CTDE) has been proven to be an effective paradigm in cooperative multi-agent reinforcement learning (MARL). One of the major chal…
Real-time Model Predictive Control and System Identification Using Differentiable Physics Simulation
Sirui Chen, Keenon Werling, Albert Wu +1
Developing robot controllers in a simulated environment is advantageous but transferring the controllers to the target environment presents challenges, often referred to as the "si…
DiffSRL: Learning Dynamical State Representation for Deformable Object Manipulation with Differentiable Simulator
Sirui Chen, Yunhao Liu, Jialong Li +3
Dynamic state representation learning is an important task in robot learning. Latent space that can capture dynamics related information has wide application in areas such as accel…