8 accepted papers
Adaptation to damages and in-situ physical repairs is essential for long-term robot autonomy, yet challenging outside of narrowly defined and well-anticipated bounds. In this work we proprioceptively adapt to catastrophic damage in soft-actuated systems in under one minute. Architected materials are…
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 robot control. Traditional methods of controlling soft robots underutilize their configuration spaces to avoid nonlinea
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, sho…
Machine learning-based approaches for soft robot proprioception have recently gained popularity, in part due to the difficulties in modeling the relationship between sensor signals and robot shape. However, to date, there exists no systematic analysis of the required design choices to set up a machi…
Developing reliable control strategies in soft robotics requires advances in soft robot perception. However, current soft robotic sensors pose many performance limitations, and available materials and manufacturing techniques complicate soft sensorized robot design. To address these long-standing ne
Electrically-mediated actuation schemes offer great promise beyond popular pneumatic and suction based ones in soft robotics. However, they often rely on bespoke materials and manufacturing approaches that constrain design flexibility and widespread adoption. Following the recent introduction of a c
Unlike traditional robots, soft robots can intrinsically interact with their environment in a continuous, robust, and safe manner. These abilities - and the new opportunities they open - motivate the development of algorithms that provide reliable information on the nature of environmental interacti
Creating soft robots with sophisticated, autonomous capabilities requires these systems to possess reliable, on-line proprioception of 3D configuration through integrated soft sensors. We present a framework for predicting a soft robot's 3D configuration via deep learning using feedback from a soft,