Learning Symbolic Operators for Task and Motion Planning

Learning Symbolic Operators for Task and Motion Planning

Bibliográfalaš dieđut
Váldodahkkit:	Silver, Tom, Chitnis, Rohan, Tenenbaum, Joshua, Kaelbling, Leslie Pack, Lozano-Perez, Tomas
Eará dahkkit:	Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Materiálatiipa:	Artihkal
Giella:	English
Almmustuhtton:	Institute of Electrical and Electronics Engineers (IEEE) 2022
Liŋkkat:	https://hdl.handle.net/1721.1/143745

Geahča maid

FFRob: Leveraging symbolic planning for efficient task and motion planning
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Almmustuhtton: (2021)

Hierarchical Task and Motion Planning in the Now
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Almmustuhtton: (2010)

Discovering State and Action Abstractions for Generalized Task and Motion Planning
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Almmustuhtton: (2023)

Integrated Robot Task and Motion Planning in the Now
Dahkki: Kaelbling, Leslie Pack, et al.
Almmustuhtton: (2012)

FFRob: Leveraging symbolic planning for efficient task and motion planning
Dahkki: Garrett, Caelan Reed, et al.
Almmustuhtton: (2022)

Integrated robot task and motion planning in belief space
Dahkki: Kaelbling, Leslie Pack, et al.
Almmustuhtton: (2012)

Learning to guide task and motion planning using score-space representation
Dahkki: Kim, Beomjoon, et al.
Almmustuhtton: (2021)

Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks
Dahkki: Silver, Tom, et al.
Almmustuhtton: (2022)

Learning compositional models of robot skills for task and motion planning
Dahkki: Wang, Zi, et al.
Almmustuhtton: (2022)

Symbol acquisition for task-level planning
Dahkki: Konidaris, George, et al.
Almmustuhtton: (2014)

GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal Babbling
Dahkki: Chitnis, Rohan, et al.
Almmustuhtton: (2022)

Active Model Learning and Diverse Action Sampling for Task and Motion Planning
Dahkki: Wang, Zi, et al.
Almmustuhtton: (2021)

Integrated task and motion planning in belief space
Dahkki: Kaelbling, Leslie P., et al.
Almmustuhtton: (2014)

Learning When to Quit: Meta-Reasoning for Motion Planning
Dahkki: Sung, Yoonchang, et al.
Almmustuhtton: (2022)

Learning Quickly to Plan Quickly Using Modular Meta-Learning
Dahkki: Chitnis, Rohan, et al.
Almmustuhtton: (2021)

Learning to guide task and motion planning using score-space representation
Dahkki: Kim, Beomjoon, et al.
Almmustuhtton: (2021)

Sample-Based Methods for Factored Task and Motion Planning
Dahkki: Garrett, Caelan, et al.
Almmustuhtton: (2021)

FFRob: An Efficient Heuristic for Task and Motion Planning
Dahkki: Garrett, Caelan Reed, et al.
Almmustuhtton: (2017)

Learning to guide task and motion planning using score-space representation
Dahkki: Kim, Beomjoon, et al.
Almmustuhtton: (2021)

Sampling-based methods for factored task and motion planning
Dahkki: Garrett, Caelan Reed, et al.
Almmustuhtton: (2020)

Look Before You Sweep: Visibility-Aware Motion Planning
Dahkki: Goretkin, Gustavo, et al.
Almmustuhtton: (2022)

Adversarial Actor-Critic Method for Task and Motion Planning Problems Using Planning Experience
Dahkki: Kim, Beomjoon, et al.
Almmustuhtton: (2021)

Integrating Human-Provided Information into Belief State Representation Using Dynamic Factorization
Dahkki: Chitnis, Rohan(Rohan Sunil), et al.
Almmustuhtton: (2021)

Symbol acquisition for probabilistic high-level planning
Dahkki: Konidaris, George, et al.
Almmustuhtton: (2018)

Online Replanning in Belief Space for Partially Observable Task and Motion Problems
Dahkki: Garrett, Caelan Reed, et al.
Almmustuhtton: (2021)

Constructing Symbolic Representations for High-Level Planning
Dahkki: Konidaris, George D., et al.
Almmustuhtton: (2016)

Learning composable models of parameterized skills
Dahkki: Kaelbling, Leslie Pack, et al.
Almmustuhtton: (2021)

PDDLStream: Integrating symbolic planners and blackbox samplers via optimistic adaptive planning
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Almmustuhtton: (2021)

Focused Model-Learning and Planning for Non-Gaussian Continuous State-Action Systems
Dahkki: Wang, Zi, et al.
Almmustuhtton: (2021)

Object placement as inverse motion planning
Dahkki: Holladay, Anne E., et al.
Almmustuhtton: (2014)

Neuro-Symbolic Learning for Bilevel Robot Planning
Dahkki: Silver, Tom
Almmustuhtton: (2024)

Learning Grammatical Models for Object Recognition
Dahkki: Aycinena, Meg, et al.
Almmustuhtton: (2008)

Finding Frequent Entities in Continuous Data
Dahkki: Alet, Ferran, et al.
Almmustuhtton: (2021)

Finding aircraft collision-avoidance strategies using policy search methods
Dahkki: Kaelbling, Leslie Pack, et al.
Almmustuhtton: (2009)

Pre-image Backchaining in Belief Space for Mobile Manipulation
Dahkki: Kaelbling, Leslie Pack, et al.
Almmustuhtton: (2021)

Few-Shot Bayesian Imitation Learning with Logical Program Policies
Dahkki: Silver, Tom, et al.
Almmustuhtton: (2021)

Task-Driven Tactile Exploration
Dahkki: Kaelbling, Leslie P., et al.
Almmustuhtton: (2011)

Learning to rank for synthesizing planning heuristics
Dahkki: Garrett, Caelan Reed, et al.
Almmustuhtton: (2018)

LQR-RRT*: Optimal sampling-based motion planning with automatically derived extension heuristics
Dahkki: Perez, Alejandro, et al.
Almmustuhtton: (2014)

Shape-Based Transfer of Generic Skills
Dahkki: Thompson, Skye, et al.
Almmustuhtton: (2022)