5 papers
A Reward-Free Viewpoint on Multi-Objective Reinforcement Learning
Ying-Tu Chen, Wei Hung, Bing-Shu Wu +2
Many sequential decision-making tasks involve optimizing multiple conflicting objectives, requiring policies that adapt to different user preferences. In multi-objective reinforcem…
Pushing Forward Pareto Frontiers of Proactive Agents with Behavioral Agentic Optimization
Yihang Yao, Zhepeng Cen, Haohong Lin +6
Proactive large language model (LLM) agents aim to actively plan, query, and interact over multiple turns, enabling efficient task completion beyond passive instruction following a…
ReGen: Generative Robot Simulation via Inverse Design
Phat Nguyen, Tsun-Hsuan Wang, Zhang-Wei Hong +5
Simulation plays a key role in scaling robot learning and validating policies, but constructing simulations remains a labor-intensive process. This paper introduces ReGen, a genera…
Embodied Red Teaming for Auditing Robotic Foundation Models
Sathwik Karnik, Zhang-Wei Hong, Nishant Abhangi +5
Language-conditioned robot models have the potential to enable robots to perform a wide range of tasks based on natural language instructions. However, assessing their safety and e…
Text-to-Drive: Diverse Driving Behavior Synthesis via Large Language Models
Phat Nguyen, Tsun-Hsuan Wang, Zhang-Wei Hong +2
Generating varied scenarios through simulation is crucial for training and evaluating safety-critical systems, such as autonomous vehicles. Yet, the task of modeling the trajectori…