most citedTowards Uncertainty Unification: A Case Study for Preference Learning

2 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

cs.RO2026

HapticWorld: an Interactive World Simulator with Real-time Torque Feedback

Shaoting Peng, Litian Liang, Yixuan Wang +4

Contact-rich manipulation depends on force sensing that is hard to infer from visual signals alone, both for collecting demonstrations and for training policies. Force-annotated da…

cs.RO2025

Tactile-Based Human Intent Recognition for Robot Assistive Navigation

Shaoting Peng, Dakarai Crowder, Wenzhen Yuan +1

Robot assistive navigation (RAN) is critical for enhancing the mobility and independence of the growing population of mobility-impaired individuals. However, existing systems often…

cs.RO2025

Trust-Aware Embodied Bayesian Persuasion for Mixed-Autonomy

Shaoting Peng, Katherine Driggs-Campbell, Roy Dong

Safe and efficient interaction between autonomous vehicles (AVs) and human-driven vehicles (HVs) is a critical challenge for future transportation systems. While game-theoretic mod…

cs.RO2025

COMMET: A System for Human-Induced Conflicts in Mobile Manipulation of Everyday Tasks

Dongping Li, Shaoting Peng, John Pohovey +1

Continuous advancements in robotics and AI are driving the integration of robots from industry into everyday environments. However, dynamic and unpredictable human activities in da…

cs.RO2025★ 2 cited

Towards Uncertainty Unification: A Case Study for Preference Learning

Shaoting Peng, Haonan Chen, Katherine Driggs-Campbell

Learning human preferences is essential for human-robot interaction, as it enables robots to adapt their behaviors to align with human expectations and goals. However, the inherent…