5 papers · 1 filter
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…
Active Exploration for Real-Time Haptic Training
Jake Ketchum, Ahalya Prabhakar, Todd D. Murphey
Tactile perception is important for robotic systems that interact with the world through touch. Touch is an active sense in which tactile measurements depend on the contact propert…