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07/13/2023
Embodied Lifelong Learning for Task and Motion Planning
A robot deployed in a home over long stretches of time faces a true life...
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06/22/2023
DiMSam: Diffusion Models as Samplers for Task and Motion Planning under Partial Observability
Task and Motion Planning (TAMP) approaches are effective at planning lon...
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03/09/2023
PDSketch: Integrated Planning Domain Programming and Learning
This paper studies a model learning and online planning approach towards...
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02/07/2023
Local Neural Descriptor Fields: Locally Conditioned Object Representations for Manipulation
A robot operating in a household environment will see a wide range of un...
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12/06/2022
Visibility-Aware Navigation Among Movable Obstacles
In this paper, we examine the problem of visibility-aware robot navigati...
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11/17/2022
SE(3)-Equivariant Relational Rearrangement with Neural Descriptor Fields
We present a method for performing tasks involving spatial relations bet...
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08/16/2022
Learning Operators with Ignore Effects for Bilevel Planning in Continuous Domains
Bilevel planning, in which a high-level search over an abstraction of an...
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07/30/2022
Robust Planning for Multi-stage Forceful Manipulation
Multi-step forceful manipulation tasks, such as opening a push-and-twist...
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06/21/2022
Learning Neuro-Symbolic Skills for Bilevel Planning
Decision-making is challenging in robotics environments with continuous ...
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06/06/2022
Fully Persistent Spatial Data Structures for Efficient Queries in Path-Dependent Motion Planning Applications
Motion planning is a ubiquitous problem that is often a bottleneck in ro...
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04/21/2022
PG3: Policy-Guided Planning for Generalized Policy Generation
A longstanding objective in classical planning is to synthesize policies...
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03/17/2022
Inventing Relational State and Action Abstractions for Effective and Efficient Bilevel Planning
Effective and efficient planning in continuous state and action spaces i...
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03/09/2022
Representation, learning, and planning algorithms for geometric task and motion planning
We present a framework for learning to guide geometric task and motion p...
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12/21/2021
Specifying and achieving goals in open uncertain robot-manipulation domains
This paper describes an integrated solution to the problem of describing...
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10/28/2021
From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence
Machine learning has long since become a keystone technology, accelerati...
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09/23/2021
Discovering State and Action Abstractions for Generalized Task and Motion Planning
Generalized planning accelerates classical planning by finding an algori...
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08/09/2021
Long-Horizon Manipulation of Unknown Objects via Task and Motion Planning with Estimated Affordances
We present a strategy for designing and building very general robot mani...
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07/01/2021
Active Learning of Abstract Plan Feasibility
Long horizon sequential manipulation tasks are effectively addressed hie...
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05/28/2021
Learning Neuro-Symbolic Relational Transition Models for Bilevel Planning
Despite recent, independent progress in model-based reinforcement learni...
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03/07/2021
Learning When to Quit: Meta-Reasoning for Motion Planning
Anytime motion planners are widely used in robotics. However, the relati...
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02/28/2021
Learning Symbolic Operators for Task and Motion Planning
Robotic planning problems in hybrid state and action spaces can be solve...
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01/07/2021
Planning for Multi-stage Forceful Manipulation
Multi-stage forceful manipulation tasks, such as twisting a nut on a bol...
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11/06/2020
Learning Online Data Association
When an agent interacts with a complex environment, it receives a stream...
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10/02/2020
Integrated Task and Motion Planning
The problem of planning for a robot that operates in environments contai...
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09/22/2020
Tailoring: encoding inductive biases by optimizing unsupervised objectives at prediction time
From CNNs to attention mechanisms, encoding inductive biases into neural...
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09/11/2020
Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks
Real-world planning problems often involve hundreds or even thousands of...
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07/26/2020
CAMPs: Learning Context-Specific Abstractions for Efficient Planning in Factored MDPs
Meta-planning, or learning to guide planning from experience, is a promi...
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06/08/2020
Learning compositional models of robot skills for task and motion planning
The objective of this work is to augment the basic abilities of a robot ...
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06/06/2020
Visual Prediction of Priors for Articulated Object Interaction
Exploration in novel settings can be challenging without prior experienc...
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03/11/2020
Meta-learning curiosity algorithms
We hypothesize that curiosity is a mechanism found by evolution that enc...
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02/06/2020
Scalable and Probabilistically Complete Planning for Robotic Spatial Extrusion
There is increasing demand for automated systems that can fabricate 3D s...
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01/22/2020
GLIB: Exploration via Goal-Literal Babbling for Lifted Operator Learning
We address the problem of efficient exploration for learning lifted oper...
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11/11/2019
Online Replanning in Belief Space for Partially Observable Task and Motion Problems
To solve multi-step manipulation tasks in the real world, an autonomous ...
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10/01/2019
Omnipush: accurate, diverse, real-world dataset of pushing dynamics with RGB-D video
Pushing is a fundamental robotic skill. Existing work has shown how to e...
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09/30/2019
Learning Compact Models for Planning with Exogenous Processes
We address the problem of approximate model minimization for MDPs in whi...
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05/28/2019
Differentiable Algorithm Networks for Composable Robot Learning
This paper introduces the Differentiable Algorithm Network (DAN), a comp...
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04/18/2019
Graph Element Networks: adaptive, structured computation and memory
We explore the use of graph neural networks (GNNs) to model spatial proc...
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01/18/2019
Look before you sweep: Visibility-aware motion planning
This paper addresses the problem of planning for a robot with a directio...
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12/19/2018
Modular meta-learning in abstract graph networks for combinatorial generalization
Modular meta-learning is a new framework that generalizes to unseen data...
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09/20/2018
Learning Quickly to Plan Quickly Using Modular Meta-Learning
Multi-object manipulation problems in continuous state and action spaces...
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07/26/2018
Learning to guide task and motion planning using score-space representation
In this paper, we propose a learning algorithm that speeds up the search...
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06/26/2018
Modular meta-learning
Many prediction problems, such as those that arise in the context of rob...
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05/21/2018
Planning to Give Information in Partially Observed Domains with a Learned Weighted Entropy Model
In many real-world robotic applications, an autonomous agent must act wi...
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05/08/2018
Finding Frequent Entities in Continuous Data
In many applications that involve processing high-dimensional data, it i...
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03/02/2018
Active model learning and diverse action sampling for task and motion planning
The objective of this work is to augment the basic abilities of a robot ...
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02/28/2018
Integrating Human-Provided Information Into Belief State Representation Using Dynamic Factorization
In partially observed environments, it can be useful for a human to prov...
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02/23/2018
STRIPStream: Integrating Symbolic Planners and Blackbox Samplers
Many planning applications involve complex relationships defined on high...
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01/02/2018
Sampling-Based Methods for Factored Task and Motion Planning
This paper presents a general-purpose formulation of a large class of di...
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11/04/2017
Guiding the search in continuous state-action spaces by learning an action sampling distribution from off-target samples
In robotics, it is essential to be able to plan efficiently in high-dime...
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01/01/2017