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

Author:

Curtis Aidan1,Fang Xiaolin1,Kaelbling Leslie Pack1,Lozano-Perez Tomas1,Garrett Caelan Reed2

Affiliation:

1. CSAIL, MIT,USA

2. NVIDIA

Funder

NSF

AFOSR

ONR

Publisher

IEEE

Reference35 articles.

1. Neural Task Programming: Learning to Generalize Across Hierarchical Tasks

2. Mt-opt: Continuous multi-task robotic reinforcement learning at scale;kalashnkov;ArXiv,2021

3. good robot!”: Efficient reinforcement learning for multi-step visual tasks via reward shaping;hundt;CoRR,2019

4. Deep dynamics models for learning dexterous manipulation;nagabandi;Conference on Robot Learning (CoRL),0

5. Task-Driven Perception and Manipulation for Constrained Placement of Unknown Objects

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1. Optimistic Reinforcement Learning-Based Skill Insertions for Task and Motion Planning;IEEE Robotics and Automation Letters;2024-06

2. COAST: COnstraints And STreams for Task and Motion Planning;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

3. Accelerating Long-Horizon Planning with Affordance-Directed Dynamic Grounding of Abstract Strategies;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

4. Out of Sight, Still in Mind: Reasoning and Planning about Unobserved Objects with Video Tracking Enabled Memory Models;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

5. Modular Multi-Level Replanning TAMP Framework for Dynamic Environment;IEEE Robotics and Automation Letters;2024-05

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