141 citations · 638 across the 52 of their papers we have counts for
62 papers
Functional Risk Minimization
Ferran Alet, Clement Gehring, Tomás Lozano-Pérez +3
The field of Machine Learning has changed significantly since the 1970s. However, its most basic principle, Empirical Risk Minimization (ERM), remains unchanged. We propose Functio…
Neural Relational Inference with Fast Modular Meta-learning
Ferran Alet, Erica Weng, Tomás Lozano Pérez +1
\textit{Graph neural networks} (GNNs) are effective models for many dynamical systems consisting of entities and relations. Although most GNN applications assume a single type of e…
Compositional Diffusion-Based Continuous Constraint Solvers
Zhutian Yang, Jiayuan Mao, Yilun Du +4
This paper introduces an approach for learning to solve continuous constraint satisfaction problems (CCSP) in robotic reasoning and planning. Previous methods primarily rely on han…
Embodied Lifelong Learning for Task and Motion Planning
Jorge Mendez-Mendez, Leslie Pack Kaelbling, Tomás Lozano-Pérez
A robot deployed in a home over long stretches of time faces a true lifelong learning problem. As it seeks to provide assistance to its users, the robot should leverage any accumul…
DiMSam: Diffusion Models as Samplers for Task and Motion Planning under Partial Observability
Xiaolin Fang, Caelan Reed Garrett, Clemens Eppner +3
Generative models such as diffusion models, excel at capturing high-dimensional distributions with diverse input modalities, e.g. robot trajectories, but are less effective at mult…
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…
Local Neural Descriptor Fields: Locally Conditioned Object Representations for Manipulation
Ethan Chun, Yilun Du, Anthony Simeonov +2
A robot operating in a household environment will see a wide range of unique and unfamiliar objects. While a system could train on many of these, it is infeasible to predict all th…
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…
Visibility-Aware Navigation Among Movable Obstacles
Jose Muguira-Iturralde, Aidan Curtis, Yilun Du +2
In this paper, we examine the problem of visibility-aware robot navigation among movable obstacles (VANAMO). A variant of the well-known NAMO robotic planning problem, VANAMO puts…
SE(3)-Equivariant Relational Rearrangement with Neural Descriptor Fields
Anthony Simeonov, Yilun Du, Lin Yen-Chen +4
We present a method for performing tasks involving spatial relations between novel object instances initialized in arbitrary poses directly from point cloud observations. Our frame…
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…
Robust Planning for Multi-stage Forceful Manipulation
Rachel Holladay, Tomás Lozano-Pérez, Alberto Rodriguez
Multi-step forceful manipulation tasks, such as opening a push-and-twist childproof bottle, require a robot to make various planning choices that are substantially impacted by the…
Fully Persistent Spatial Data Structures for Efficient Queries in Path-Dependent Motion Planning Applications
Sathwik Karnik, Tomás Lozano-Pérez, Leslie Pack Kaelbling +1
Motion planning is a ubiquitous problem that is often a bottleneck in robotic applications. We demonstrate that motion planning problems such as minimum constraint removal, belief-…
Learning Neuro-Symbolic Skills for Bilevel Planning
Tom Silver, Ashay Athalye, Joshua B. Tenenbaum +2
Decision-making is challenging in robotics environments with continuous object-centric states, continuous actions, long horizons, and sparse feedback. Hierarchical approaches, such…
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…
Representation, learning, and planning algorithms for geometric task and motion planning
Beomjoon Kim, Luke Shimanuki, Leslie Pack Kaelbling +1
We present a framework for learning to guide geometric task and motion planning (GTAMP). GTAMP is a subclass of task and motion planning in which the goal is to move multiple objec…
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…
Specifying and achieving goals in open uncertain robot-manipulation domains
Leslie Pack Kaelbling, Alex LaGrassa, Tomás Lozano-Pérez
This paper describes an integrated solution to the problem of describing and interpreting goals for robots in open uncertain domains. Given a formal specification of a desired situ…
From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence
Nicholas Roy, Ingmar Posner, Tim Barfoot +17
Machine learning has long since become a keystone technology, accelerating science and applications in a broad range of domains. Consequently, the notion of applying learning metho…
Discovering State and Action Abstractions for Generalized Task and Motion Planning
Aidan Curtis, Tom Silver, Joshua B. Tenenbaum +2
Generalized planning accelerates classical planning by finding an algorithm-like policy that solves multiple instances of a task. A generalized plan can be learned from a few train…
Long-Horizon Manipulation of Unknown Objects via Task and Motion Planning with Estimated Affordances
Aidan Curtis, Xiaolin Fang, Leslie Pack Kaelbling +2
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…
Active Learning of Abstract Plan Feasibility
Michael Noseworthy, Caris Moses, Isaiah Brand +4
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…
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…
Learning When to Quit: Meta-Reasoning for Motion Planning
Yoonchang Sung, Leslie Pack Kaelbling, Tomás Lozano-Pérez
Anytime motion planners are widely used in robotics. However, the relationship between their solution quality and computation time is not well understood, and thus, determining whe…