In this paper, we introduce a sensor designed for robotic fingers which can provide information on the displacements induced by external forces. Our sensor uses LEDs to sense the displacement between two plates connected by a transparent elastomer; when a force is applied to the finger, the elastomer displaces and the LED signals change. We show that using LEDs as both light emitters and receivers in this context provides high sensitivity, allowing such an emitter and receiver pairs to detect very small displacements. We characterize the standalone performance of the sensor by testing the ability of a supervised learning model to predict complete force and torque data from its raw signals, and obtain a mean error between 0.05 and 0.07 N across the three directions of force applied to the finger. Our method allows for compact packaging (fitting at the base of a finger) with no amplification electronics, low cost manufacturing, easy integration into a complete hand, and high overload shear forces and bending torques, suggesting future applicability to complete manipulation tasks.

Underactuated manipulators reduce the number of bulky motors, thereby enabling compact and mechanically robust designs. However, fewer actuators than joints means that the manipulator can only access a specific manifold within the joint space, which is particular to a given hardware configuration and can be low-dimensional and/or discontinuous. Determining an appropriate set of hardware parameters for this class of mechanisms, therefore, is difficult - even for traditional task-based co-optimization methods. In this paper, our goal is to implement a task-based design and policy co-optimization method for underactuated, tendon-driven manipulators. We first formulate a general model for an underactuated, tendon-driven transmission. We then use this model to co-optimize a three-link, two-actuator kinematic chain using reinforcement learning. We demonstrate that our optimized tendon transmission and control policy can be transferred reliably to physical hardware with real-world reaching experiments.

We present simplified 2D dynamic models of the 3D, passive dynamic inspired walking gait of a physical quasi-passive walking robot. Quasi-passive walkers are robots that integrate passive walking principles and some form of actuation. Our ultimate goal is to better understand the dynamics of actuated walking in order to create miniature, untethered, bipedal walking robots. At these smaller scales there is limited space and power available, and so in this work we leverage the passive dynamics of walking to reduce the burden on the actuators and controllers. Prior quasi-passive walkers are much larger than our intended scale, have more complicated mechanical designs, and require more precise feedback control and/or learning algorithms. By leveraging the passive 3D dynamics, carefully designing the spherical feet, and changing the actuation scheme, we are able to produce a very simple 3D bipedal walking model that has a total of 5 rigid bodies and a single actuator per leg. Additionally, the model requires no feedback as each actuator is controlled by an open-loop sinusoidal profile. We validate this model in 2D simulations in which we measure the stability properties while varying the leg length/amplitude ratio, the frequency of actuation, and the spherical foot profile. These results are also validated experimentally on a 3D walking robot (15cm leg length) that implements the modeled walking dynamics. Finally, we experimentally investigate the ability to control the heading of the robot by changing the open-loop control parameters of the robot.