Exoskeleton-covered soft finger with vision-based proprioception and tactile sensing

Soft robots offer significant advantages in adaptability, safety, and dexterity compared to conventional rigid-body robots. However, it is challenging to equip soft robots with accurate proprioception and tactile sensing due to their high flexibility and elasticity. In this work, we describe the development of a vision-based proprioceptive and tactile sensor for soft robots called GelFlex, which is inspired by previous GelSight sensing techniques. More specifically, we develop a novel exoskeleton-covered soft finger with embedded cameras and deep learning methods that enable high-resolution proprioceptive sensing and rich tactile sensing. To do so, we design features along the axial direction of the finger, which enable high-resolution proprioceptive sensing, and incorporate a reflective ink coating on the surface of the finger to enable rich tactile sensing. We design a highly underactuated exoskeleton with a tendon-driven mechanism to actuate the finger. Finally, we assemble 2 of the fingers together to form a robotic gripper and successfully perform a bar stock classification task, which requires both shape and tactile information. We train neural networks for proprioception and shape (box versus cylinder) classification using data from the embedded sensors. The proprioception CNN had over 99\% accuracy on our testing set (all six joint angles were within 1 degree of error) and had an average accumulative distance error of 0.77 mm during live testing, which is better than human finger proprioception. These proposed techniques offer soft robots the high-level ability to simultaneously perceive their proprioceptive state and peripheral environment, providing potential solutions for soft robots to solve everyday manipulation tasks. We believe the methods developed in this work can be widely applied to different designs and applications.Comment: Accepted to ICRA202

Sensing, Design Optimization, and Motion Planning for Agile Pneumatic Artificial Muscle-Driven Robots

Mechanical compliance in robotic systems facilitates safe human-robot interaction and improves robot adaptation to environmental uncertainty. Several promising compliant actuator technologies have emerged from the field of soft robotics, in particular the pneumatic artificial muscle—a soft, lightweight actuator that contracts under pressure. The pneumatic muscle's passive compliance eliminates the need for precise high-bandwidth actuator control to simulate mechanical impedance. However, the pneumatic muscle is limited in practical robot applications—particularly, without sacrificing robot agility—due to several key challenges: development of compatible soft sensors, translation of conventional high-level control and planning techniques to pneumatic muscle-driven systems, and limitations in pneumatic muscle pressurization rate and force generation capabilities. This work seeks to address these challenges, via a threefold approach, to access the benefits of compliant robot actuation while maximizing the robot's dynamic capabilities. The first objective targets the development of a pneumatic muscle design with integrated sensing to enable kinematic and dynamic state estimation of muscle-actuated robots without hindering muscle compliance. The second objective focuses on the construction of a trajectory optimization framework for planning dynamic robot maneuvers using 'burst-inflation' muscle pressure control. Finally, the third objective explores a design optimization strategy utilizing biological joint mechanisms to compensate for pneumatic muscle limitations and maximize robot agility.Ph.D