GPU Based Path Integral Control with Learned Dynamics

We present an algorithm which combines recent advances in model based path integral control with machine learning approaches to learning forward dynamics models. We take advantage of the parallel computing power of a GPU to quickly take a massive number of samples from a learned probabilistic dynamics model, which we use to approximate the path integral form of the optimal control. The resulting algorithm runs in a receding-horizon fashion in realtime, and is subject to no restrictive assumptions about costs, constraints, or dynamics. A simple change to the path integral control formulation allows the algorithm to take model uncertainty into account during planning, and we demonstrate its performance on a quadrotor navigation task. In addition to this novel adaptation of path integral control, this is the first time that a receding-horizon implementation of iterative path integral control has been run on a real system.Comment: 6 pages, NIPS 2014 - Autonomously Learning Robots Worksho

Autoencoder-based myoelectric controller for prosthetic hands

In the past, linear dimensionality-reduction techniques, such as Principal Component Analysis, have been used to simplify the myoelectric control of high-dimensional prosthetic hands. Nonetheless, their nonlinear counterparts, such as Autoencoders, have been shown to be more effective at compressing and reconstructing complex hand kinematics data. As a result, they have a potential of being a more accurate tool for prosthetic hand control. Here, we present a novel Autoencoder-based controller, in which the user is able to control a high-dimensional (17D) virtual hand via a low-dimensional (2D) space. We assess the efficacy of the controller via a validation experiment with four unimpaired participants. All the participants were able to significantly decrease the time it took for them to match a target gesture with a virtual hand to an average of 6.9s and three out of four participants significantly improved path efficiency. Our results suggest that the Autoencoder-based controller has the potential to be used to manipulate high-dimensional hand systems via a myoelectric interface with a higher accuracy than PCA; however, more exploration needs to be done on the most effective ways of learning such a controller

Sensuator: A Hybrid Sensor–Actuator Approach to Soft Robotic Proprioception Using Recurrent Neural Networks

Soft robotic actuators are now being used in practical applications; however, they are often limited to open-loop control that relies on the inherent compliance of the actuator. Achieving human-like manipulation and grasping with soft robotic actuators requires at least some form of sensing, which often comes at the cost of complex fabrication and purposefully built sensor structures. In this paper, we utilize the actuating fluid itself as a sensing medium to achieve high-fidelity proprioception in a soft actuator. As our sensors are somewhat unstructured, their readings are difficult to interpret using linear models. We therefore present a proof of concept of a method for deriving the pose of the soft actuator using recurrent neural networks. We present the experimental setup and our learned state estimator to show that our method is viable for achieving proprioception and is also robust to common sensor failures

Negshell casting: 3D-printed structured and sacrificial cores for soft robot fabrication.

Soft robot fabrication by casting liquid elastomer often requires multiple steps of casting or skillful manual labor. We present a novel soft robotic fabrication technique: negshell casting (negative-space eggshell casting), that reduces the steps required for fabrication by introducing 3D-printed thin-walled cores for use in casting that are meant to be left in place instead of being removed later in the fabrication process. Negshell casting consists of two types of cores: sacrificial cores (negshell cores) and structural cores. Negshell cores are designed to be broken into small pieces that have little effect on the mechanical structure of the soft robot, and can be used for creating fluidic channels and bellows for actuation. Structural cores, on the other hand, are not meant to be broken, and are for increasing the stiffness of soft robotic structures, such as endoskeletons. We describe the design and fabrication concepts for both types of cores and report the mechanical characterization of the cores embedded in silicone rubber specimens. We also present an example use-case of negshell casting for a single joint soft robotic finger, along with an experiment to demonstrate how negshell casting concepts can aid in force transmission. Finally, we present real-world usage of negshell casting in a 6 degree-of-freedom three-finger soft robotic gripper, and a demonstration of the gripper in a robotic pick-and-place task. A companion website with further details about fabrication (as well as an introduction to molding and casting for those who are unfamiliar with the terms), engineering file downloads, and experimental data is provided at https://negshell.github.io/

Reduced Dimensionality Control for the ACT Hand

Abstract — Redundant tendon-driven systems such as the human hand or the ACT robotic hand are high-dimensional and nonlinear systems that make traditional control strategies ineffective. The synergy hypothesis from neuroscience suggests that employing dimensionality reduction techniques can simplify the system without a major loss in function. We define a dimensionality reduction framework consisting of separate observation and activation synergies, a first-order model, and an optimal controller. The framework is implemented for two example tasks: adaptive control of thumb posture and hybrid position/force control to enable dynamic handwriting. I

Tendon-driven variable impedance control using reinforcement learning

Abstract—Biological motor control is capable of learning complex movements containing contact transitions and unknown force requirements while adapting the impedance of the system. In this work, we seek to achieve robotic mimicry of this compliance, employing stiffness only when it is necessary for task completion. We use path integral reinforcement learning which has been successfully applied on torque-driven systems to learn episodic tasks without using explicit models. Applying this method to tendon-driven systems is challenging because of the increase in dimensionality, the intrinsic nonlinearities of such systems, and the increased effect of external dynamics on the lighter tendon-driven end effectors. We demonstrate the simultaneous learning of feedback gains and desired tendon trajectories in a dynamically complex slidingswitch task with a tendon-driven robotic hand. The learned controls look noisy but nonetheless result in smooth and expert task performance. We show discovery of dynamic strategies not explored in a demonstration, and that the learned strategy is useful for understanding difficult-to-model plant characteristics. I

Tendon-Driven Control of Biomechanical and Robotic Systems: A Path Integral Reinforcement Learning Approach.

Abstract — We apply path integral reinforcement learning to a biomechanically accurate dynamics model of the index finger and then to the Anatomically Correct Testbed (ACT) robotic hand. We illustrate the applicability of Policy Improvement with Path Integrals (P I 2) to parameterized and non-parameterized control policies. This method is based on sampling variations in control, executing them in the real world, and minimizing a cost function on the resulting performance. Iteratively improving the control policy based on real-world performance requires no direct modeling of tendon network nonlinearities and contact transitions, allowing improved task performance. I

A Non-Laboratory Gait Dataset of Full Body Kinematics and Egocentric Vision

Abstract In this manuscript, we describe a unique dataset of human locomotion captured in a variety of out-of-the-laboratory environments captured using Inertial Measurement Unit (IMU) based wearable motion capture. The data contain full-body kinematics for walking, with and without stops, stair ambulation, obstacle course navigation, dynamic movements intended to test agility, and negotiating common obstacles in public spaces such as chairs. The dataset contains 24.2 total hours of movement data from a college student population with an approximately equal split of males to females. In addition, for one of the activities, we captured the egocentric field of view and gaze of the subjects using an eye tracker. Finally, we provide some examples of applications using the dataset and discuss how it might open possibilities for new studies in human gait analysis