141 citations · 638 across the 52 of their papers we have counts for
31 papers · 1 filter
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…
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…
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…
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…
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…
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 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…
Learning Symbolic Operators for Task and Motion Planning
Tom Silver, Rohan Chitnis, Joshua Tenenbaum +2
Robotic planning problems in hybrid state and action spaces can be solved by integrated task and motion planners (TAMP) that handle the complex interaction between motion-level dec…
Planning for Multi-stage Forceful Manipulation
Rachel Holladay, Tomás Lozano-Pérez, Alberto Rodriguez
Multi-stage forceful manipulation tasks, such as twisting a nut on a bolt, require reasoning over interlocking constraints over discrete as well as continuous choices. The robot mu…
Integrated Task and Motion Planning
Caelan Reed Garrett, Rohan Chitnis, Rachel Holladay +4
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 sta…
Visual Prediction of Priors for Articulated Object Interaction
Caris Moses, Michael Noseworthy, Leslie Pack Kaelbling +2
Exploration in novel settings can be challenging without prior experience in similar domains. However, humans are able to build on prior experience quickly and efficiently. Childre…
Learning compositional models of robot skills 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 sensorimotor primitives to solve complex long-horizon manipulation problems. This require…
Scalable and Probabilistically Complete Planning for Robotic Spatial Extrusion
Caelan Reed Garrett, Yijiang Huang, Tomás Lozano-Pérez +1
There is increasing demand for automated systems that can fabricate 3D structures. Robotic spatial extrusion has become an attractive alternative to traditional layer-based 3D prin…
Online Replanning in Belief Space for Partially Observable Task and Motion Problems
Caelan Reed Garrett, Chris Paxton, Tomás Lozano-Pérez +2
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. This may require op…
Omnipush: accurate, diverse, real-world dataset of pushing dynamics with RGB-D video
Maria Bauza, Ferran Alet, Yen-Chen Lin +4
Pushing is a fundamental robotic skill. Existing work has shown how to exploit models of pushing to achieve a variety of tasks, including grasping under uncertainty, in-hand manipu…