Soft DAgger: Sample-Efficient Imitation Learning for Control of Soft Robots.

ABSTRACT: This paper presents Soft DAgger, an efficient imitation learning-based approach for training control solutions for soft robots. To demonstrate the effectiveness of the proposed algorithm, we implement it on a two-module soft robotic arm involved in the task of writing letters in 3D space. Soft DAgger uses a dynamic behavioral map of the soft robot, which maps the robot's task space to its actuation space. The map acts as a teacher and is responsible for predicting the optimal actions for the soft robot based on its previous state action history, expert demonstrations, and current position. This algorithm achieves generalization ability without depending on costly exploration techniques or reinforcement learning-based synthetic agents. We propose two variants of the control algorithm and de

SUBMITTER: Nazeer MS 

PROVIDER: S-EPMC10574889 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature