10 citations · 39 across the 18 of their papers we have counts for
4 papers · 1 filter
Towards Optimal Correlational Object Search
Kaiyu Zheng, Rohan Chitnis, Yoonchang Sung +2
In realistic applications of object search, robots will need to locate target objects in complex environments while coping with unreliable sensors, especially for small or hard-to-…
Reinforcement Learning for Classical Planning: Viewing Heuristics as Dense Reward Generators
Clement Gehring, Masataro Asai, Rohan Chitnis +4
Recent advances in reinforcement learning (RL) have led to a growing interest in applying RL to classical planning domains or applying classical planning methods to some complex RL…
Learning Neuro-Symbolic Relational Transition Models for Bilevel Planning
Rohan Chitnis, Tom Silver, Joshua B. Tenenbaum +2
In robotic domains, learning and planning are complicated by continuous state spaces, continuous action spaces, and long task horizons. In this work, we address these challenges wi…
Learning Symbolic Operators for Task and Motion Planning
Tom Silver, Rohan Chitnis, Joshua Tenenbaum +2
Robotic planning problems in hybrid state and action spaces can be solved by integrated task and motion planners (TAMP) that handle the complex interaction between motion-level dec…