141 citations · 638 across the 52 of their papers we have counts for
10 papers · 1 filter
Modular meta-learning in abstract graph networks for combinatorial generalization
Ferran Alet, Maria Bauza, Alberto Rodriguez +2
Modular meta-learning is a new framework that generalizes to unseen datasets by combining a small set of neural modules in different ways. In this work we propose abstract graph ne…
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…
Learning to guide task and motion planning using score-space representation
Beomjoon Kim, Zi Wang, Leslie Pack Kaelbling +1
In this paper, we propose a learning algorithm that speeds up the search in task and motion planning problems. Our algorithm proposes solutions to three different challenges that a…
Modular meta-learning
Ferran Alet, Tomás Lozano-Pérez, Leslie P. Kaelbling
Many prediction problems, such as those that arise in the context of robotics, have a simplifying underlying structure that, if known, could accelerate learning. In this paper, we…
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…
Active model learning and diverse action sampling for task and motion planning
Zi Wang, Caelan Reed Garrett, Leslie Pack Kaelbling +1
The objective of this work is to augment the basic abilities of a robot by learning to use new sensorimotor primitives to enable the solution of complex long-horizon problems. Solv…
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…
Sampling-Based Methods for Factored Task and Motion Planning
Caelan Reed Garrett, Tomás Lozano-Pérez, Leslie Pack Kaelbling
This paper presents a general-purpose formulation of a large class of discrete-time planning problems, with hybrid state and control-spaces, as factored transition systems. Factori…