A Method of Generating Robot Motion using Common Motion Trajectory Model and Function Information of Tools-Reference-Cited by-同舟云学术

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A Method of Generating Robot Motion using Common Motion Trajectory Model and Function Information of Tools

Published:2023-09-01 Issue:9 Volume:143 Page:877-884
ISSN:0385-4221
Container-title:IEEJ Transactions on Electronics, Information and Systems
language:en
Short-container-title:IEEJ Trans.EIS

Author:

Ando Yuta¹,Suzuki Takahiro¹,Akizuki Shuichi¹,Hashimoto Manabu¹

Affiliation:

1. Chukyo University

Publisher

Institute of Electrical Engineers of Japan (IEE Japan)

Subject

Electrical and Electronic Engineering

Link

https://www.jstage.jst.go.jp/article/ieejeiss/143/9/143_877/_pdf

Reference12 articles.

1. (1) M. Kokic, J. A. Stork, J. A. Haustein, and D. Kragic: “Affordance Detection for Task-Specific Grasping Using Deep Learning”, In 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (2017)

2. (2) T. Patten, K. Park, and M. Vincze: DGCM-Net: Dense Geometrical Correspondence Matching Network for Incremental Experience-Based Robotic Grasping, Frontiers in Robotics and AI (2020)

3. (3) P. Mandikal and K. Grauman: “Learning dexterous grasping with object-centric visual affordances”, IEEE International Conference on Robotics and Automation (ICRA), pp. 6169-6176 (2021)

4. (4) M. Kokic, D. Kragic, and J. Bohg: “Learning Task-Oriented Grasping From Human Activity Datasets”, IEEE Robotics and Automation Letters, pp. 3352-3359 (2020)

5. (5) F. Chu, R. Xu, and P. A. Vela: “Real-world Multi-object, Multi-grasp Detection”, IEEE Robotics and Automation Letters, pp. 2377-3766 (2018)

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