1 code implementation • 23 May 2023 • Prajjwal Bhargava, Rohan Chitnis, Alborz Geramifard, Shagun Sodhani, Amy Zhang
Our key findings are: (1) Sequence Modeling requires more data than Q-Learning to learn competitive policies but is more robust; (2) Sequence Modeling is a substantially better choice than both Q-Learning and Imitation Learning in sparse-reward and low-quality data settings; and (3) Sequence Modeling and Imitation Learning are preferable as task horizon increases, or when data is obtained from human demonstrators.
1 code implementation • 16 Aug 2022 • Nishanth Kumar, Willie McClinton, Rohan Chitnis, Tom Silver, Tomás Lozano-Pérez, Leslie Pack Kaelbling
Bilevel planning, in which a high-level search over an abstraction of an environment is used to guide low-level decision-making, is an effective approach to solving long-horizon tasks in continuous state and action spaces.
1 code implementation • 17 Mar 2022 • Tom Silver, Rohan Chitnis, Nishanth Kumar, Willie McClinton, Tomas Lozano-Perez, Leslie Pack Kaelbling, Joshua Tenenbaum
Our key idea is to learn predicates by optimizing a surrogate objective that is tractable but faithful to our real efficient-planning objective.
1 code implementation • 19 Oct 2021 • Kaiyu Zheng, Rohan Chitnis, Yoonchang Sung, George Konidaris, Stefanie Tellex
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-detect objects.
no code implementations • 30 Sep 2021 • Clement Gehring, Masataro Asai, Rohan Chitnis, Tom Silver, Leslie Pack Kaelbling, Shirin Sohrabi, Michael Katz
In this paper, we propose to leverage domain-independent heuristic functions commonly used in the classical planning literature to improve the sample efficiency of RL.
2 code implementations • AAAI Workshop CLeaR 2022 • Rohan Chitnis, Tom Silver, Joshua B. Tenenbaum, Tomas Lozano-Perez, Leslie Pack Kaelbling
In robotic domains, learning and planning are complicated by continuous state spaces, continuous action spaces, and long task horizons.
1 code implementation • 28 Feb 2021 • Tom Silver, Rohan Chitnis, Joshua Tenenbaum, Leslie Pack Kaelbling, Tomas Lozano-Perez
We then propose a bottom-up relational learning method for operator learning and show how the learned operators can be used for planning in a TAMP system.
no code implementations • 2 Oct 2020 • Caelan Reed Garrett, Rohan Chitnis, Rachel Holladay, Beomjoon Kim, Tom Silver, Leslie Pack Kaelbling, Tomás Lozano-Pérez
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 state of the objects, is known as task and motion planning (TAMP).
1 code implementation • 11 Sep 2020 • Tom Silver, Rohan Chitnis, Aidan Curtis, Joshua Tenenbaum, Tomas Lozano-Perez, Leslie Pack Kaelbling
We conclude that learning to predict a sufficient set of objects for a planning problem is a simple, powerful, and general mechanism for planning in large instances.
1 code implementation • 26 Jul 2020 • Rohan Chitnis, Tom Silver, Beomjoon Kim, Leslie Pack Kaelbling, Tomas Lozano-Perez
A general meta-planning strategy is to learn to impose constraints on the states considered and actions taken by the agent.
2 code implementations • 15 Feb 2020 • Tom Silver, Rohan Chitnis
We present PDDLGym, a framework that automatically constructs OpenAI Gym environments from PDDL domains and problems.
no code implementations • ICLR 2020 • Rohan Chitnis, Shubham Tulsiani, Saurabh Gupta, Abhinav Gupta
Our key idea is that a good guiding principle for intrinsic motivation in synergistic tasks is to take actions which affect the world in ways that would not be achieved if the agents were acting on their own.
1 code implementation • 22 Jan 2020 • Rohan Chitnis, Tom Silver, Joshua Tenenbaum, Leslie Pack Kaelbling, Tomas Lozano-Perez
We address the problem of efficient exploration for transition model learning in the relational model-based reinforcement learning setting without extrinsic goals or rewards.
no code implementations • 30 Sep 2019 • Rohan Chitnis, Tomás Lozano-Pérez
We address the problem of approximate model minimization for MDPs in which the state is partitioned into endogenous and (much larger) exogenous components.
no code implementations • 30 Sep 2019 • Rohan Chitnis, Shubham Tulsiani, Saurabh Gupta, Abhinav Gupta
Our insight is that for many tasks, the learning process can be decomposed into learning a state-independent task schema (a sequence of skills to execute) and a policy to choose the parameterizations of the skills in a state-dependent manner.
1 code implementation • 20 Sep 2018 • Rohan Chitnis, Leslie Pack Kaelbling, Tomás Lozano-Pérez
Multi-object manipulation problems in continuous state and action spaces can be solved by planners that search over sampled values for the continuous parameters of operators.
no code implementations • 21 May 2018 • Rohan Chitnis, Leslie Pack Kaelbling, Tomás Lozano-Pérez
We consider such a setting in which the agent can, while acting, transmit declarative information to the human that helps them understand aspects of this unseen environment.
no code implementations • 8 May 2018 • Ferran Alet, Rohan Chitnis, Leslie P. Kaelbling, Tomas Lozano-Perez
In many applications that involve processing high-dimensional data, it is important to identify a small set of entities that account for a significant fraction of detections.
no code implementations • 28 Feb 2018 • Rohan Chitnis, Leslie Pack Kaelbling, Tomás Lozano-Pérez
In partially observed environments, it can be useful for a human to provide the robot with declarative information that represents probabilistic relational constraints on properties of objects in the world, augmenting the robot's sensory observations.