Imitation Learning Based on Bilateral Control for Human-Robot Cooperation
On the map: Offline RL and imitation learning from data → Robot manipulation imitation learning
Abstract (arXiv)
Robots are required to autonomously respond to changing situations. Imitation learning is a promising candidate for achieving generalization performance, and extensive results have been demonstrated in object manipulation. However, cooperative work between humans and robots is still a challenging issue because robots must control dynamic interactions among themselves, humans, and objects. Furthermore, it is difficult to follow subtle perturbations that may occur among coworkers. In this study, we find that cooperative work can be accomplished by imitation learning using bilateral control. Thanks to bilateral control, which can extract response values and command values independently, human skills to control dynamic interactions can be extracted. Then, the task of serving food is considered. The experimental results clearly demonstrate the importance of force control, and the dynamic interactions can be controlled by the inferred action force.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 12 of its 31 references that are in the map. First posted on arXiv 28 Sep 2019.
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12 of 31 references.
References · 12
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- 2019Time Series Motion Generation Considering Long Short-Term Motioncited by 10
- 2019Improving dual-arm assembly by master-slave compliancecited by 3
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- 2023Soft and Rigid Object Grasping With Cross-Structure Hand Using Bilateral Control-Based Imitation Learning
- 2025Robotic Manipulation via Imitation Learning: Taxonomy, Evolution, Benchmark, and Challenges
- 2021Bilateral Control-Based Imitation Learning for Velocity-Controlled Robot
- 2024Bi-ACT: Bilateral Control-Based Imitation Learning via Action Chunking with Transformer
- 2022Eliciting Compatible Demonstrations for Multi-Human Imitation Learning
- 2025Interactive Imitation Learning for Dexterous Robotic Manipulation: Challenges and Perspectives -- A Survey
- 2024A Comparison of Imitation Learning Algorithms for Bimanual Manipulation
- 2024ALPHA-α and Bi-ACT Are All You Need: Importance of Position and Force Information/Control for Imitation Learning of Unimanual and Bimanual Robotic Manipulation with Low-Cost System