Behavior Cloning from Observations with Domain Mapping for the Control of Soft Robots
Beatrice Tosi, Muhammad Sunny Nazeer, Egidio Falotico · 2024
The designs of soft robots often draw inspiration from biological systems, even though their morphologies and actuation mechanisms often differ significantly. Reproducing bio-inspired movements on a corresponding platform neces-sitates in-depth knowledge of these mechanisms. This paper introduces a simplified approach to tackle this challenge in an imitation learning-based problem setting. Leveraging a human arm as an expert, state-only paths were recorded and quantitatively replicated on a pneumatically actuated two-module soft arm. To overcome morphological differences, both bodies were segmented into a fixed number of segments. Various mapping strategies were explored to project the observed workspace of each segment from the demonstrator's body onto the corresponding operational workspace of the learner. A platform-specific model was trained to predict actions for reaching the mapped expert demonstrations. Ultimately, a policy trained from these state-action pairs using behavioral cloning successfully imitated the expert demonstrations, achieving a mean MAE consistently under 3% of the overall workspace of the soft robotic arm. On average, the proposed algorithm achieved above 79% mean MAE reduction in 16 episodes, with each episode approximately 7 sec long, across three different tasks.