1 citations · 1 across the 5 of their papers we have counts for
5 papers
MANIGUARD: A Benchmark and Data Suite for Specification-Grounded Safety Evaluation and Improvement of Robotic Manipulation
Yiyan Peng, Philip Wang, Simon Sinong Zhan +11
Foundation-model policies for robotic manipulation are advancing rapidly on task success, but rigorous evaluation of whether they succeed safely is still lacking. We introduce Mani…
SENTINEL: A Multi-Level Formal Framework for Safety Evaluation of Foundation Model-based Embodied Agents
Simon Sinong Zhan, Philip Wang, Yao Liu +13
We present SENTINEL, a framework for formally evaluating the physical safety of foundation model (FM)-based embodied agents. SENTINEL is the first to provide multi-level safety eva…
Belief-Based Offline Reinforcement Learning for Delay-Robust Policy Optimization
Simon Sinong Zhan, Qingyuan Wu, Philip Wang +4
Offline-to-online deployment of reinforcement-learning (RL) agents must bridge two gaps: (1) the sim-to-real gap, where real systems add latency and other imperfections not present…
Inverse Delayed Reinforcement Learning
Simon Sinong Zhan, Qingyuan Wu, Zhian Ruan +6
Inverse Reinforcement Learning (IRL) has demonstrated effectiveness in a variety of imitation tasks. In this paper, we introduce an IRL framework designed to extract rewarding feat…
Enhancing Inverse Reinforcement Learning through Encoding Dynamic Information in Reward Shaping
Simon Sinong Zhan, Philip Wang, Qingyuan Wu +4
In this paper, we aim to tackle the limitation of the Adversarial Inverse Reinforcement Learning (AIRL) method in stochastic environments where theoretical results cannot hold and…