8 citations · 13 across the 2 of their papers we have counts for
13 papers
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…
Integrated Task and Motion Planning
Caelan Reed Garrett, Rohan Chitnis, Rachel Holladay +4
The problem of planning for a robot that operates in environments containing a large number of objects, taking actions to move itself through the world as well as to change the sta…
Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks
Tom Silver, Rohan Chitnis, Aidan Curtis +3
Real-world planning problems often involve hundreds or even thousands of objects, straining the limits of modern planners. In this work, we address this challenge by learning to pr…
CAMPs: Learning Context-Specific Abstractions for Efficient Planning in Factored MDPs
Rohan Chitnis, Tom Silver, Beomjoon Kim +2
Meta-planning, or learning to guide planning from experience, is a promising approach to improving the computational cost of planning. A general meta-planning strategy is to learn…
Intrinsic Motivation for Encouraging Synergistic Behavior
Rohan Chitnis, Shubham Tulsiani, Saurabh Gupta +1
We study the role of intrinsic motivation as an exploration bias for reinforcement learning in sparse-reward synergistic tasks, which are tasks where multiple agents must work toge…
PDDLGym: Gym Environments from PDDL Problems
Tom Silver, Rohan Chitnis
We present PDDLGym, a framework that automatically constructs OpenAI Gym environments from PDDL domains and problems. Observations and actions in PDDLGym are relational, making the…