Search Results for author: Tomas Lozano-Perez

Found 24 papers, 11 papers with code

SE(3)-Equivariant Relational Rearrangement with Neural Descriptor Fields

1 code implementation17 Nov 2022 Anthony Simeonov, Yilun Du, Lin Yen-Chen, Alberto Rodriguez, Leslie Pack Kaelbling, Tomas Lozano-Perez, Pulkit Agrawal

This formalism is implemented in three steps: assigning a consistent local coordinate frame to the task-relevant object parts, determining the location and orientation of this coordinate frame on unseen object instances, and executing an action that brings these frames into the desired alignment.

Learning Neuro-Symbolic Skills for Bilevel Planning

no code implementations21 Jun 2022 Tom Silver, Ashay Athalye, Joshua B. Tenenbaum, Tomas Lozano-Perez, Leslie Pack Kaelbling

Decision-making is challenging in robotics environments with continuous object-centric states, continuous actions, long horizons, and sparse feedback.

Decision Making Motion Planning +1

PG3: Policy-Guided Planning for Generalized Policy Generation

1 code implementation21 Apr 2022 Ryan Yang, Tom Silver, Aidan Curtis, Tomas Lozano-Perez, Leslie Pack Kaelbling

In this work, we study generalized policy search-based methods with a focus on the score function used to guide the search over policies.

Predicate Invention for Bilevel Planning

1 code implementation17 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.

Learning Symbolic Operators for Task and Motion Planning

1 code implementation28 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.

Motion Planning Operator learning +2

Learning Object-Based State Estimators for Household Robots

no code implementations6 Nov 2020 Yilun Du, Tomas Lozano-Perez, Leslie Kaelbling

The robot may be called upon later to retrieve objects and will need a long-term object-based memory in order to know how to find them.

Clustering Representation Learning +1

Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks

1 code implementation11 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.

Motion Planning Task and Motion Planning

Meta-learning curiosity algorithms

1 code implementation ICLR 2020 Ferran Alet, Martin F. Schneider, Tomas Lozano-Perez, Leslie Pack Kaelbling

We hypothesize that curiosity is a mechanism found by evolution that encourages meaningful exploration early in an agent's life in order to expose it to experiences that enable it to obtain high rewards over the course of its lifetime.

Acrobot Meta-Learning

GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal Babbling

1 code implementation22 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.

Decision Making Efficient Exploration +3

Differentiable Algorithm Networks for Composable Robot Learning

no code implementations28 May 2019 Peter Karkus, Xiao Ma, David Hsu, Leslie Pack Kaelbling, Wee Sun Lee, Tomas Lozano-Perez

This paper introduces the Differentiable Algorithm Network (DAN), a composable architecture for robot learning systems.


Graph Element Networks: adaptive, structured computation and memory

2 code implementations18 Apr 2019 Ferran Alet, Adarsh K. Jeewajee, Maria Bauza, Alberto Rodriguez, Tomas Lozano-Perez, Leslie Pack Kaelbling

We explore the use of graph neural networks (GNNs) to model spatial processes in which there is no a priori graphical structure.

Modular meta-learning in abstract graph networks for combinatorial generalization

1 code implementation19 Dec 2018 Ferran Alet, Maria Bauza, Alberto Rodriguez, Tomas Lozano-Perez, Leslie P. Kaelbling

Modular meta-learning is a new framework that generalizes to unseen datasets by combining a small set of neural modules in different ways.


Finding Frequent Entities in Continuous Data

no code implementations8 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.


Guiding the search in continuous state-action spaces by learning an action sampling distribution from off-target samples

no code implementations4 Nov 2017 Beomjoon Kim, Leslie Pack Kaelbling, Tomas Lozano-Perez

For such complex planning problems, unguided uniform sampling of actions until a path to a goal is found is hopelessly inefficient, and gradient-based approaches often fall short when the optimization manifold of a given problem is not smooth.

Backward-Forward Search for Manipulation Planning

no code implementations12 Apr 2016 Caelan Reed Garrett, Tomas Lozano-Perez, Leslie Pack Kaelbling

In this paper we address planning problems in high-dimensional hybrid configuration spaces, with a particular focus on manipulation planning problems involving many objects.

Solving the multiple instance problem with axis-parallel rectangles

no code implementations Artificial Intelligence 1997 Thomas G. Dietteric, Richard H. Lathrop, Tomas Lozano-Perez

The multiple instance problem arises in tasks where the training examples are ambiguous: a single example object may have many alternative feature vectors (instances) that describe it, and yet only one of those feature vectors may be responsible for the observed classification of the object.

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