Automated Gait Generation For Walking, Soft Robotic Quadrupeds
Jake Ketchum, Sophia Schiffer, Muchen Sun, Pranav Kaarthik, Ryan L., Truby, Todd D. Murphey

TL;DR
This paper introduces a novel, efficient, and simulation-free method for autonomous gait generation in soft robots, demonstrated on a quadruped with minimal computation and no prior simulation or user input.
Contribution
It presents the first fully autonomous gait generation approach for soft robots, using a minimal, sample-efficient method without simulation or complex computation.
Findings
Generates effective gaits in as little as 4 minutes of experimentation
Outperforms hand-crafted gaits in translation and rotation
First demonstration of autonomous gait generation in a soft robot
Abstract
Gait generation for soft robots is challenging due to the nonlinear dynamics and high dimensional input spaces of soft actuators. Limitations in soft robotic control and perception force researchers to hand-craft open loop controllers for gait sequences, which is a non-trivial process. Moreover, short soft actuator lifespans and natural variations in actuator behavior limit machine learning techniques to settings that can be learned on the same time scales as robot deployment. Lastly, simulation is not always possible, due to heterogeneity and nonlinearity in soft robotic materials and their dynamics change due to wear. We present a sample-efficient, simulation free, method for self-generating soft robot gaits, using very minimal computation. This technique is demonstrated on a motorized soft robotic quadruped that walks using four legs constructed from 16 "handed shearing auxetic"…
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Taxonomy
TopicsSoft Robotics and Applications · Advanced Materials and Mechanics · Modular Robots and Swarm Intelligence
