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Manufacturing, Control, and Performance Evaluation of a Gecko-Inspired Soft Robot
Published on: June 10, 2020
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Exploring Behaviors of Caterpillar-Like Soft Robots with a Central Pattern Generator-Based Controller and
Matthew Ishige1, Takuya Umedachi1, Tadahiro Taniguchi2
1Graduate School of Information Science and Technology, The University of Tokyo, Bunkyo-ku, Tokyo, Japan.
Soft Robotics
|May 21, 2019
Summary
This study introduces SenseCPG-PGPE, a novel method for automatically designing diverse behaviors in soft-bodied robots. The approach successfully optimizes controllers for complex movements and environmental interactions.
Area of Science:
- Robotics
- Control Systems
- Artificial Intelligence
Background:
- Soft-bodied robots offer behavioral flexibility but lack effective design methodologies for diverse functionalities.
- Current controllers for soft robots are often handcrafted and limited in scope.
- Robot behavior is intrinsically linked to body-environment interactions, necessitating adaptive control.
Purpose of the Study:
- To propose and investigate SenseCPG-PGPE, a method for automatic behavior design in caterpillar-like soft-bodied robots.
- To demonstrate the efficacy of policy gradients with parameter-based exploration (PGPE) in optimizing central pattern generator (CPG)-based controllers for soft robots.
- To explore the capability of the proposed method in achieving diverse and adaptive behaviors.
Main Methods:
- Developed SenseCPG-PGPE, which optimizes mechanosensory feedback to a CPG-based controller using PGPE.
- Investigated the performance of PGPE against other policy gradient methods (TRPO, PPO) for robots with viscoelasticity and large deformation.
- Validated the method's scalability and ability to generate non-steady motions through simulation.
Main Results:
- PGPE effectively optimized CPG controllers for soft robots, outperforming TRPO and PPO.
- The method enabled simulated robots to perform non-steady motions like climbing steps.
- SenseCPG-PGPE generated distinct behaviors tailored to different body-environment conditions, showcasing adaptability.
Conclusions:
- SenseCPG-PGPE successfully enables soft robots to automatically explore and achieve a variety of behaviors.
- The proposed method is robust for controlling soft robots with complex physical properties.
- This work advances the field of soft robotics by providing a powerful tool for behavior generation.
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