TY - GEN
T1 - Goal Inference via Corrective Path Demonstration for Human-Robot Collaboration
AU - Ohnishi, Fumiya
AU - Kawasaki, Yosuke
AU - Takahashi, Masaki
N1 - Funding Information:
Acknowledgments. This work was supported by Core Research for Evolutional Science and Technology (CREST) of the Japan Science and Technology Agency (JST) [grant number JPMJCR19A1].
Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Recently, collaborative robots, such as collaborative delivery robots, have been expected to improve the work efficiency of users. For natural human-robot collaboration, it is necessary to infer the appropriate goal position to transport instruments, where the user’s convenience and the surrounding environment are considered. In conventional research, the goal is inferred by demonstrating the user’s desired positions, but position demonstration requires many trials to obtain the inference model, which is burdensome for the user. Therefore, we focus on the user’s correction of the robot position and generate multiple position samples from the user’s corrective path. In addition, these position samples are weighted based on the implicit intention of the correction to learn both the desired and undesired positions. Consequently, the robot improves goal inference in fewer trials. The effectiveness of the proposed method was evaluated by experiment that simulated human-robot collaborative environments.
AB - Recently, collaborative robots, such as collaborative delivery robots, have been expected to improve the work efficiency of users. For natural human-robot collaboration, it is necessary to infer the appropriate goal position to transport instruments, where the user’s convenience and the surrounding environment are considered. In conventional research, the goal is inferred by demonstrating the user’s desired positions, but position demonstration requires many trials to obtain the inference model, which is burdensome for the user. Therefore, we focus on the user’s correction of the robot position and generate multiple position samples from the user’s corrective path. In addition, these position samples are weighted based on the implicit intention of the correction to learn both the desired and undesired positions. Consequently, the robot improves goal inference in fewer trials. The effectiveness of the proposed method was evaluated by experiment that simulated human-robot collaborative environments.
KW - Goal inference
KW - Human-robot collaboration
KW - Learning from demonstration
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U2 - 10.1007/978-3-031-22216-0_2
DO - 10.1007/978-3-031-22216-0_2
M3 - Conference contribution
AN - SCOPUS:85148726489
SN - 9783031222153
T3 - Lecture Notes in Networks and Systems
SP - 15
EP - 28
BT - Intelligent Autonomous Systems 17 - Proceedings of the 17th International Conference IAS-17
A2 - Petrovic, Ivan
A2 - Markovic, Ivan
A2 - Menegatti, Emanuele
PB - Springer Science and Business Media Deutschland GmbH
T2 - 17th International Conference on Intelligent Autonomous Systems, IAS-17
Y2 - 13 June 2022 through 16 June 2022
ER -