4 papers
Real-Time Reinforcement Learning for Dynamic Tasks with a Parallel Soft Robot
James Avtges, Jake Ketchum, Millicent Schlafly +5
Closed-loop control remains an open challenge in soft robotics. The nonlinear responses of soft actuators under dynamic loading conditions limit the use of analytic models for soft…
Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates
Zixin Zhang, James Avtges, Todd D. Murphey
Data-driven control methods need to be sample-efficient and lightweight, especially when data acquisition and computational resources are limited -- such as during learning on hard…
Force and Speed in a Soft Stewart Platform
Jake Ketchum, James Avtges, Millicent Schlafly +4
Many soft robots struggle to produce dynamic motions with fast, large displacements. We develop a parallel 6 degree-of-freedom (DoF) Stewart-Gough mechanism using Handed Shearing A…
Embodied Active Learning of Generative Sensor-Object Models
Allison Pinosky, Todd D. Murphey
When a robot encounters a novel object, how should it respond$\unicode{x2014}$what data should it collect$\unicode{x2014}$so that it can find the object in the future? In this work…