mlsanityData to 7 Oct 2026

Motion ReTouch: Motion Modification Using Four-Channel Bilateral Control

arXiv 2502.20982 ↗First posted 28 Feb 2025cs.ROcited by 2

On the map: Offline RL and imitation learning from data → Robot manipulation imitation learning

Abstract (arXiv)

Recent research has demonstrated the usefulness of imitation learning in autonomous robot operation. In particular, teaching using four-channel bilateral control, which can obtain position and force information, has been proven effective. However, control performance that can easily execute high-speed, complex tasks in one go has not yet been achieved. We propose a method called Motion ReTouch, which retroactively modifies motion data obtained using four-channel bilateral control. The proposed method enables modification of not only position but also force information. This was achieved by the combination of multilateral control and motion-copying system. The proposed method was verified in experiments with a real robot, and the success rate of the test tube transfer task was improved, demonstrating the possibility of modification force information.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data. First posted on arXiv 28 Feb 2025.

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Citing papers per year: 2026: 2.

2 papers.

  1. 2026Refinement of Accelerated Demonstrations via Incremental Iterative Reference Learning Control for Fast Contact-Rich Imitation Learningcited by 1
  2. 2026How to Utilize Failure Demo Data?: Effective Data Selection for Imitation Learning Using Distribution Differences in Attention Mechanismcited by 0

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  1. 2025Design and Experimental Validation of Sensorless 4-Channel Bilateral Teleoperation for Low-Cost Manipulators
  2. 2021Bilateral Control-Based Imitation Learning for Velocity-Controlled Robot
  3. 2021Imitation learning for variable speed motion generation over multiple actions
  4. 2018Imitation Learning for Object Manipulation Based on Position/Force Information Using Bilateral Control
  5. 2026Feasibility-aware Imitation Learning from Observation with Multimodal Feedback
  6. 2025Robotic Manipulation via Imitation Learning: Taxonomy, Evolution, Benchmark, and Challenges
  7. 2024Bi-ACT: Bilateral Control-Based Imitation Learning via Action Chunking with Transformer
  8. 2024ReMoBot: Retrieval-Based Few-Shot Imitation Learning for Mobile Manipulation with Vision Foundation Models