141 citations · 638 across the 52 of their papers we have counts for
18 papers · 1 filter
Learning Rational Subgoals from Demonstrations and Instructions
Zhezheng Luo, Jiayuan Mao, Jiajun Wu +3
We present a framework for learning useful subgoals that support efficient long-term planning to achieve novel goals. At the core of our framework is a collection of rational subgo…
PDSketch: Integrated Planning Domain Programming and Learning
Jiayuan Mao, Tomás Lozano-Pérez, Joshua B. Tenenbaum +1
This paper studies a model learning and online planning approach towards building flexible and general robots. Specifically, we investigate how to exploit the locality and sparsity…
Learning Efficient Abstract Planning Models that Choose What to Predict
Nishanth Kumar, Willie McClinton, Rohan Chitnis +3
An effective approach to solving long-horizon tasks in robotics domains with continuous state and action spaces is bilevel planning, wherein a high-level search over an abstraction…
PG3: Policy-Guided Planning for Generalized Policy Generation
Ryan Yang, Tom Silver, Aidan Curtis +2
A longstanding objective in classical planning is to synthesize policies that generalize across multiple problems from the same domain. In this work, we study generalized policy se…
Predicate Invention for Bilevel Planning
Tom Silver, Rohan Chitnis, Nishanth Kumar +4
Efficient planning in continuous state and action spaces is fundamentally hard, even when the transition model is deterministic and known. One way to alleviate this challenge is to…
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…
GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal Babbling
Rohan Chitnis, Tom Silver, Joshua Tenenbaum +2
We address the problem of efficient exploration for transition model learning in the relational model-based reinforcement learning setting without extrinsic goals or rewards. Inspi…
Learning Quickly to Plan Quickly Using Modular Meta-Learning
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. The e…
Finding Frequent Entities in Continuous Data
Ferran Alet, Rohan Chitnis, Leslie P. Kaelbling +1
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. Rathe…
Learning What Information to Give in Partially Observed Domains
Rohan Chitnis, Leslie Pack Kaelbling, Tomás Lozano-Pérez
In many robotic applications, an autonomous agent must act within and explore a partially observed environment that is unobserved by its human teammate. We consider such a setting…
Integrating Human-Provided Information Into Belief State Representation Using Dynamic Factorization
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 propertie…
PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning
Caelan Reed Garrett, Tomás Lozano-Pérez, Leslie Pack Kaelbling
Many planning applications involve complex relationships defined on high-dimensional, continuous variables. For example, robotic manipulation requires planning with kinematic, coll…
Guiding the search in continuous state-action spaces by learning an action sampling distribution from off-target samples
Beomjoon Kim, Leslie Pack Kaelbling, Tomas Lozano-Perez
In robotics, it is essential to be able to plan efficiently in high-dimensional continuous state-action spaces for long horizons. For such complex planning problems, unguided unifo…
STRIPS Planning in Infinite Domains
Caelan Reed Garrett, Tomás Lozano-Pérez, Leslie Pack Kaelbling
Many robotic planning applications involve continuous actions with highly non-linear constraints, which cannot be modeled using modern planners that construct a propositional repre…
Learning to Rank for Synthesizing Planning Heuristics
Caelan Reed Garrett, Leslie Pack Kaelbling, Tomas Lozano-Perez
We investigate learning heuristics for domain-specific planning. Prior work framed learning a heuristic as an ordinary regression problem. However, in a greedy best-first search, t…
Focused Model-Learning and Planning for Non-Gaussian Continuous State-Action Systems
Zi Wang, Stefanie Jegelka, Leslie Pack Kaelbling +1
We introduce a framework for model learning and planning in stochastic domains with continuous state and action spaces and non-Gaussian transition models. It is efficient because (…
Object-based World Modeling in Semi-Static Environments with Dependent Dirichlet-Process Mixtures
Lawson L. S. Wong, Thanard Kurutach, Leslie Pack Kaelbling +1
To accomplish tasks in human-centric indoor environments, robots need to represent and understand the world in terms of objects and their attributes. We refer to this attribute-bas…
CAPIR: Collaborative Action Planning with Intention Recognition
Truong-Huy Dinh Nguyen, David Hsu, Wee-Sun Lee +4
We apply decision theoretic techniques to construct non-player characters that are able to assist a human player in collaborative games. The method is based on solving Markov decis…