TY - GEN
T1 - Structured Motion Generation with Predictive Learning
T2 - 2023 IEEE International Conference on Robotics and Automation, ICRA 2023
AU - Saito, Namiko
AU - Moura, Joao
AU - Ogata, Tetsuya
AU - Aoyama, Marina Y.
AU - Murata, Shingo
AU - Sugano, Shigeki
AU - Vijayakumar, Sethu
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - For assisting humans in their daily lives, robots need to perform long-horizon tasks, such as tidying up a room or preparing a meal. One effective strategy for handling a long-horizon task is to break it down into short-horizon subgoals, that the robot can execute sequentially. In this paper, we propose extending a predictive learning model using deep neural networks (DNN) with a Subgoal Proposal Module (SPM), with the goal of making such tasks realizable. We evaluate our proposed model in a case-study of a long-horizon task, consisting of cutting and arranging a pizza. This task requires the robot to consider: (1) the order of the subtasks, (2) multiple subtask selection, (3) coordination of dual-arm, and (4) variations within a subtask. The results confirm that the model is able to generalize motion generation to unseen tools and objects arrangement combinations. Furthermore, it significantly reduces the prediction error of the generated motions compared to without the proposed SPM. Finally, we validate the generated motions on the dual-arm robot Nextage Open. See our accompanying video here: https://youtu.be/3hYS2knRm50
AB - For assisting humans in their daily lives, robots need to perform long-horizon tasks, such as tidying up a room or preparing a meal. One effective strategy for handling a long-horizon task is to break it down into short-horizon subgoals, that the robot can execute sequentially. In this paper, we propose extending a predictive learning model using deep neural networks (DNN) with a Subgoal Proposal Module (SPM), with the goal of making such tasks realizable. We evaluate our proposed model in a case-study of a long-horizon task, consisting of cutting and arranging a pizza. This task requires the robot to consider: (1) the order of the subtasks, (2) multiple subtask selection, (3) coordination of dual-arm, and (4) variations within a subtask. The results confirm that the model is able to generalize motion generation to unseen tools and objects arrangement combinations. Furthermore, it significantly reduces the prediction error of the generated motions compared to without the proposed SPM. Finally, we validate the generated motions on the dual-arm robot Nextage Open. See our accompanying video here: https://youtu.be/3hYS2knRm50
UR - http://www.scopus.com/inward/record.url?scp=85168667636&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85168667636&partnerID=8YFLogxK
U2 - 10.1109/ICRA48891.2023.10161046
DO - 10.1109/ICRA48891.2023.10161046
M3 - Conference contribution
AN - SCOPUS:85168667636
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 9566
EP - 9572
BT - Proceedings - ICRA 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 29 May 2023 through 2 June 2023
ER -