Search Results for author: Tomás Lozano-Pérez

Found 24 papers, 9 papers with code

Learning Rational Subgoals from Demonstrations and Instructions

no code implementations9 Mar 2023 Zhezheng Luo, Jiayuan Mao, Jiajun Wu, Tomás Lozano-Pérez, Joshua B. Tenenbaum, Leslie Pack Kaelbling

We present a framework for learning useful subgoals that support efficient long-term planning to achieve novel goals.

PDSketch: Integrated Planning Domain Programming and Learning

no code implementations9 Mar 2023 Jiayuan Mao, Tomás Lozano-Pérez, Joshua B. Tenenbaum, Leslie Pack Kaelbling

This paper studies a model learning and online planning approach towards building flexible and general robots.

Overcoming the Pitfalls of Prediction Error in Operator Learning for Bilevel Planning

1 code implementation16 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.

Decision Making Operator learning

Representation, learning, and planning algorithms for geometric task and motion planning

no code implementations9 Mar 2022 Beomjoon Kim, Luke Shimanuki, Leslie Pack Kaelbling, Tomás Lozano-Pérez

The first is an algorithm for learning a rank function that guides the discrete task level search, and the second is an algorithm for learning a sampler that guides the continuous motionlevel search.

Motion Planning Representation Learning

Long-Horizon Manipulation of Unknown Objects via Task and Motion Planning with Estimated Affordances

no code implementations9 Aug 2021 Aidan Curtis, Xiaolin Fang, Leslie Pack Kaelbling, Tomás Lozano-Pérez, Caelan Reed Garrett

We present a strategy for designing and building very general robot manipulation systems involving the integration of a general-purpose task-and-motion planner with engineered and learned perception modules that estimate properties and affordances of unknown objects.

Grasp Generation Motion Planning +1

Active Learning of Abstract Plan Feasibility

no code implementations1 Jul 2021 Michael Noseworthy, Caris Moses, Isaiah Brand, Sebastian Castro, Leslie Kaelbling, Tomás Lozano-Pérez, Nicholas Roy

Long horizon sequential manipulation tasks are effectively addressed hierarchically: at a high level of abstraction the planner searches over abstract action sequences, and when a plan is found, lower level motion plans are generated.

Active Learning

Planning for Multi-stage Forceful Manipulation

no code implementations7 Jan 2021 Rachel Holladay, Tomás Lozano-Pérez, Alberto Rodriguez

The robot must choose a sequence of discrete actions, or strategy, such as whether to pick up an object, and the continuous parameters of each of those actions, such as how to grasp the object.


Integrated Task and Motion Planning

no code implementations2 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).

Motion Planning

Neural Relational Inference with Fast Modular Meta-learning

1 code implementation NeurIPS 2019 Ferran Alet, Erica Weng, Tomás Lozano-Pérez, Leslie Pack Kaelbling

Framing inference as the inner-loop optimization of meta-learning leads to a model-based approach that is more data-efficient and capable of estimating the state of entities that we do not observe directly, but whose existence can be inferred from their effect on observed entities.


Online Replanning in Belief Space for Partially Observable Task and Motion Problems

1 code implementation11 Nov 2019 Caelan Reed Garrett, Chris Paxton, Tomás Lozano-Pérez, Leslie Pack Kaelbling, Dieter Fox

To solve multi-step manipulation tasks in the real world, an autonomous robot must take actions to observe its environment and react to unexpected observations.

Continuous Control

Learning Compact Models for Planning with Exogenous Processes

no code implementations30 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.

Learning Quickly to Plan Quickly Using Modular Meta-Learning

1 code implementation20 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.


Modular meta-learning

1 code implementation26 Jun 2018 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.


Learning What Information to Give in Partially Observed Domains

no code implementations21 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.

Active model learning and diverse action sampling for task and motion planning

2 code implementations2 Mar 2018 Zi Wang, Caelan Reed Garrett, Leslie Pack Kaelbling, Tomás Lozano-Pérez

Solving long-horizon problems in complex domains requires flexible generative planning that can combine primitive abilities in novel combinations to solve problems as they arise in the world.

Active Learning Motion Planning

Integrating Human-Provided Information Into Belief State Representation Using Dynamic Factorization

no code implementations28 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.

PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning

4 code implementations23 Feb 2018 Caelan Reed Garrett, Tomás Lozano-Pérez, Leslie Pack Kaelbling

We extend PDDL to support a generic, declarative specification for these procedures that treats their implementation as black boxes.

Motion Planning

STRIPS Planning in Infinite Domains

4 code implementations1 Jan 2017 Caelan Reed Garrett, Tomás Lozano-Pérez, Leslie Pack Kaelbling

We introduce STRIPStream: an extension of the STRIPS language which can model these domains by supporting the specification of blackbox generators to handle complex constraints.

Motion Planning

Focused Model-Learning and Planning for Non-Gaussian Continuous State-Action Systems

no code implementations26 Jul 2016 Zi Wang, Stefanie Jegelka, Leslie Pack Kaelbling, Tomás Lozano-Pérez

We introduce a framework for model learning and planning in stochastic domains with continuous state and action spaces and non-Gaussian transition models.

Bayesian Optimization with Exponential Convergence

no code implementations NeurIPS 2015 Kenji Kawaguchi, Leslie Pack Kaelbling, Tomás Lozano-Pérez

This paper presents a Bayesian optimization method with exponential convergence without the need of auxiliary optimization and without the delta-cover sampling.

Object-based World Modeling in Semi-Static Environments with Dependent Dirichlet-Process Mixtures

no code implementations2 Dec 2015 Lawson L. S. Wong, Thanard Kurutach, Leslie Pack Kaelbling, Tomás Lozano-Pérez

We refer to this attribute-based representation as a world model, and consider how to acquire it via noisy perception and maintain it over time, as objects are added, changed, and removed in the world.


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