Motion ReTouch: Motion Modification Using Four-Channel Bilateral Control (2502.20982v1)

Abstract: 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.

Overview of Motion ReTouch: Motion Modification Using Four-Channel Bilateral Control

The paper presents "Motion ReTouch," a sophisticated method for post-processing motion data obtained via four-channel bilateral control, a pivotal technique in the field of imitation learning for robotic manipulations. This method holds promise in refining robotic control operations in complex tasks where precision in motion and force is critical yet elusive using conventional methods.

Problem Context

The field of imitation learning has advanced significantly, especially in training robots to mimic human tasks by capturing and replicating motion. The four-channel bilateral control framework, which synchronizes both position and force, offers an intricate platform for developing autonomous robots capable of handling objects across varying task complexities. Despite its capabilities, traditional approaches fall short in achieving high-speed or complex task performances in a single attempt, primarily due to inaccuracies in the motion or force data being recorded.

Motion ReTouch Proposal

The authors propose "Motion ReTouch," a method leveraging a combination of multilateral control and a motion-copying system to retroactively modify recorded motion data. The method employs a unique setup with three components: a virtual leader representing the recorded motion data, a follower executing the task, and an editor robot for real-time modifications.

1. Multilateral Control Integration: This control architecture is essential for synchronizing multiple robots' spatial and force dynamics. Within Motion ReTouch, multilateral control is finely tuned to adjust robot actions based on historical and edited motion data, enhancing task accuracy without re-teleoperation.
2. Post-Editing Capabilities: A key innovation of Motion ReTouch is enabling modifications in force information, a critical aspect mostly overlooked in conventional imitation learning systems. The system facilitates correction and refinement of motion trajectories post-operation, thereby improving the learned models by aligning them more closely with the desired output.

Experimental Verification and Results

The efficacy of Motion ReTouch was empirically validated using a seven-degree-of-freedom robotic arm tasked with transferring test tubes between racks. The task included both simplistic and intricate grasp-and-place operations, inherently requiring precise motion and force control for success. The method not only enhanced the task success rate substantially when operating at speeds three times faster than normal but also yielded significant stability improvements in force feedback, crucial for maintaining task integrity under high-speed conditions.

The experiments underscored the following main outcomes:

• 100% Success Rate: With Motion ReTouch adjustments, the success rate for high-speed test tube transfer tasks improved significantly, highlighting the robustness of the proposed method in demanding scenarios.
• Stability in Force Control: Enhanced force stability was observed during critical task execution phases, particularly during test tube insertion, underscoring Motion ReTouch's ability to refine force feedback effectively.

Implications and Future Directions

The proposed Motion ReTouch illustrates a substantial advancement in the post-processing potential of imitation learning frameworks, particularly valuable for precision tasks that demand meticulous synchronization of motion and force. Practically, this flexibility can significantly enhance the adaptability of automated systems in sectors requiring delicate manipulations, such as biomedical applications or intricate assembly lines.

The paper leaves open several avenues for future exploration. Notably, adapting the leader robot to employ learned models rather than static datasets could circumvent current limitations in modifying trajectory extensively. Additionally, visualizing force data for easier human intervention, and integrating real-time temporal adjustments could enhance the system's adaptability further, paving the way for broader deployments in dynamically evolving task environments.

In conclusion, Motion ReTouch introduces a critical methodology for upgrading imitation learning systems, particularly in scenarios demanding the fine-tuning of robotic performance post-deployment. The insights and achievements demonstrated are expected to catalyze further research into refining and expanding the capabilities of bilateral control frameworks in complex real-world applications.