publications

Conference Articles

2023

1. IAS

   Goal Inference via Corrective Path Demonstration for Human-Robot Collaboration

   Fumiya Ohnishi, Yosuke Kawasaki, and Masaki Takahashi

   In Intelligent Autonomous Systems 17, 2023

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   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.

2. ROMAN

   Rush-Out Risk Mapping from Human Operational Commands Considering Field Context

   Fumiya Ohnishi, Yosuke Kawasaki, and Masaki Takahashi

   In 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 2023

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   Collaborative delivery robots in hospitals are required to move safely and efficiently in a short time, without colliding with people. Hence, they must consider the risk of people rushing out from blind spots or rooms, including field context such as the role and usage of the location. However, these factors are difficult to extract solely from geometric information. Therefore, we propose a method for generating a rush-out risk map considering the field context from the hospital staff’s operation data of an electric wheelchair. We convert the wheelchair’s speed operated by staff into rush-out risk, and then place rush-out risk potentials at positions where rush-outs may occur. Subsequently, we optimize the mapping position of rush-out risk and parameters of each potential to minimize the error to obtain a rush-out risk map. We collected actual staff operation data in the hospital and confirmed that we could generate a rush-out risk map with small errors.

3. ROBOMECH

   Motion Planning of Give-Way Behavior Along the Facility Structure for Hospital Transport Robot（施設構造に沿って道を譲る病院内搬送ロボットの動作計画）

   Fumiya Ohnishi, Yosuke Kawasaki, and Masaki Takahashi

   In The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec), 2023

2022

1. ROBOMECH

   Goal Inference via Corrective Path Demonstration for Collaborative Delivery Robot（ユーザの修正経路教示に基づく協働搬送ロボットの目的地推論）

   Fumiya ONISHI, Yosuke KAWASAKI, and Masaki TAKAHASHI

   In The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec), 2022

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   For natural human-robot collaboration, a collaborative delivery robot should infer the user’s desired goal position to transport instruments, where the user’s convenience and the surrounding environment are considered. However, it is difficult to describe all such user preferences and complex human-robot coexisting environments in advance. Therefore, we focus on the user’s corrective path of the robot position, and using this correction, the robot sequentially improves inference of the user’s desired goal position. We generate multiple position samples from the corrective path, and each position sample is weighted based on the implicit intention of the correction to learn both the desired and undesired positions. Furthermore, the inferences of desired and undesired positions are integrated. Consequently, the robot sequentially improves the goal inference in fewer trials. The effectiveness of the proposed method was evaluated using a simulator that simulates human-robot collaborative environments..