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Design and Fabrication of a Soft Robotic Hand and Arm System

We present the hardware design and fabrication of a soft arm and hand for physical human-robot interaction. The six DOF arm has two air-filled force sensing modules which passively absorb impact and provide contact force feedback. The arm has an inflated outer cover which encloses the arm’s underlying mechanisms and force sensing modules. An internal projector projects a display on the inside of the cover which is visible from the outside. On the end of the arm is a 3D printed hand with air-filled, force sensing fingertips. We validate the efficacy of the outer cover design by bending the arm…

Stickman: Towards a Human Scale Acrobatic Robot

Human performers have developed impressive acrobatic techniques over thousands of years of practicing the gymnastic arts. At the same time, robots have started to become more mobile and autonomous, and can begin to imitate these stunts in dramatic and informative ways. We present a simple two degree of freedom robot that uses a gravity-driven pendulum launch and produces a variety of somersaulting stunts. The robot uses an IMU and a laser range-finder to estimate its state mid-flight and actuates to change its motion both on and and off the pendulum. We discuss the dynamics of this behavior in a framework…

Automated Deep Reinforcement Learning Environment for Hardware of a Modular Legged Robot

In this paper, we present an automated learning environment for developing control policies directly on the hardware of a modular legged robot. This environment facilitates the reinforcement learning process by computing the rewards using a vision-based tracking system and relocating the robot to the initial position using a resetting mechanism. We employ two state-of-the-art deep reinforcement learning (DRL) algorithms, Trust Region Policy Optimization (TRPO) and Deep Deterministic Policy Gradient (DDPG), to train neural network policies for simple rowing and crawling motions. Using the developed environment, we demonstrate both learning algorithms can effectively learn policies for simple locomotion skills on highly…

Computational Design of Robotic Devices from High-Level Motion Specifications

We present a novel computational approach to designing robotic devices from high-level motion specifications. Our computational system uses a library of modular components— actuators, mounting brackets, and connectors—to define the space of possible robot designs. The process of creating a new robot begins with a set of input trajectories that specify how its end effectors and/or body should move. By searching through the combinatorial set of possible arrangements of modular components, our method generates a functional, as-simple-as possible robotic device that is capable of tracking the input motion trajectories. To significantly improve the efficiency of this discrete optimization process, we…

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