Bag All You Need: Learning a Generalizable Bagging Strategy for Heterogeneous Objects-Reference-Cited by-同舟云学术

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Bag All You Need: Learning a Generalizable Bagging Strategy for Heterogeneous Objects

Published:2023-10-01 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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Author:

Bahety Arpit¹,Jain Shreeya¹,Ha Huy¹,Hager Nathalie¹,Burchfiel Benjamin²,Cousineau Eric²,Feng Siyuan²,Song Shuran¹

Affiliation:

1. Columbia University

2. Toyota Research Institute

Funder

Toyota Research Institute

NSF

Google

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10341341/10341342/10341841.pdf?arnumber=10341841

Reference41 articles.

1. Initial results on grasping and lifting physical deformable bags with a bimanual robot;Seita,2021

2. Online 3D Bin Packing with Constrained Deep Reinforcement Learning

3. Learning practically feasible policies for online 3D bin packing

4. Form2Fit: Learning Shape Priors for Generalizable Assembly from Disassembly

5. Transporter networks: Rearranging the visual world for robotic manipulation;Zeng;arXiv preprint,2020

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Interactive Perception for Deformable Object Manipulation;IEEE Robotics and Automation Letters;2024-09

2. Learning to bag with a simulation‐free reinforcement learning framework for robots;IET Cyber-Systems and Robotics;2024-04-11

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