Skin deformation haptic devices worn on the finger pad provide realistic touch feedback during interactions with virtual objects. Two primary challenges in creating such devices are: first, making a multidegree-of-freedom device (DoF) that is small and lightweight so it does not encumber the wearer and second, providing accurate control of forces displayed to the finger pad. This work presents a 4-DoF finger pad haptic device, called Fourigami, that addresses these challenges. We address the first challenge using origami manufacturing methods and pneumatic actuation to fabricate a 25 g prototype that displays normal, shear, and twist and can be easily worn on the finger pad. We address the second challenge using a low-profile, 6-DoF, force/torque sensor to control forces displayed to the finger. Fourigami has a bandwidth ranging from 2 to 4 Hz depending on direction, and when acting on a human finger, it exerts forces ranging from$\pm$1.0 N in shear, 4.2 N in normal, and$\pm$4.2 N$\cdot$mm of twist. Finally, we demonstrate the device’s efficacy when rendering haptic feedback to a user tracking a sinusoidal trajectory and a trajectory representing interactions with a virtual object.

This paper presents a hierarchical framework for planning and control of in-hand manipulation of a rigid object involving grasp changes using fully-actuated multifin-gered robotic hands. While the framework can be applied to the general dexterous manipulation, we focus on a more complex definition of in-hand manipulation, where at the goal pose the hand has to reach a grasp suitable for using the object as a tool. The high level planner determines the object trajectory as well as the grasp changes, i.e. adding, removing, or sliding fingers, to be executed by the low-level controller. While the grasp sequence is planned online by a learning-based policy to adapt to variations, the trajectory planner and the low-level controller for object tracking and contact force control are exclusively model-based to robustly realize the plan. By infusing the knowledge about the physics of the problem and the low-level controller into the grasp planner, it learns to successfully generate grasps similar to those generated by model-based optimization approaches, obviating the high computation cost of online running of such methods to account for variations. By performing experiments in physics simulation for realistic tool use scenarios, we show the success of our method on different tool-use tasks and dexterous hand models. Additionally, we show that this hybrid method offers more robustness to trajectory and task variations compared to a model-based method.

Model-predictive control (MPC) is an increasingly popular method for controlling complex robotic systems in which optimal control problems are solved on board the robot at real-time rates. However, successful application of MPC depends critically on the performance of the algorithms used to solve the underlying optimization problems. An ideal solver should both leverage the structure of the MPC problem and support efficient "warm starting" so that information from previous solutions can be recycled to speed convergence. We present ALTRO-C, a high-performance solver with both of these properties that utilizes an augmented Lagrangian method to handle general convex conic constraints. We demonstrate the new solver’s superior performance against several existing state-of-the-art solvers on a variety of benchmark control problems formulated as both quadratic and second-order cone programs.

Simulating soft robots in cluttered environments remains an open problem due to the challenge of capturing complex dynamics and interactions with the environment. Furthermore, fast simulation is desired for quickly exploring robot behaviors in the context of motion planning. In this paper, we examine a particular class of inflated-beam soft growing robots called "vine robots," and present a dynamics simulator that captures general behaviors, handles robot-object interactions, and runs faster than real time. The simulator framework uses a simplified multi-link, rigid-body model with contact constraints. To bridge the sim-to-real gap, we develop methods for fitting model parameters based on video data of a robot in motion and in contact with an environment. We provide examples of simulations, including several with fit parameters, to show the qualitative and quantitative agreement between simulated and real behaviors. Our work demonstrates the capabilities of this high-speed dynamics simulator and its potential for use in the control of soft robots.

Robotic grasping can enable mobile vehicles to physically interact with the environment for delivery, repositioning, or landing. However, the requirements for grippers on mobile vehicles differ substantially from those used for conventional manipulation. Specifically, grippers for dynamic mobile robots should be capable of rapid activation, high force density, low power consumption, and minimal computation. In this work, we present a biologically-inspired robotic gripper designed specifically for mobile platforms. This design exploits a bistable shell to achieve “reflexive” activation based on contact with the environment. The mechanism can close its grasp within 0. 12s without any sensing or control. Electrical input power is not required for grasping or holding load. The reflexive gripper utilizes a novel pneumatic design to open its grasp with low power, and the gripper can carry slung loads up to 28 times its weight. This new mechanism, including the kinematics, static behavior, control structure, and fabrication, is described in detail. A proof of concept prototype is designed, built, and tested. Experimental results are used to characterize performance and demonstrate the potential of these methods.