Learning to Navigate Cloth using Haptics

We present a controller that allows an arm-like manipulator to navigate deformable cloth garments in simulation through the use of haptic information. The main challenge of such a controller is to avoid getting tangled in, tearing or punching through the deforming cloth. Our controller aggregates force information from a number of haptic-sensing spheres all along the manipulator for guidance. Based on haptic forces, each individual sphere updates its target location, and the conflicts that arise between this set of desired positions is resolved by solving an inverse kinematic problem with constraints. Reinforcement learning is used to train the controller for a single haptic-sensing sphere, where a training run is terminated (and thus penalized) when large forces are detected due to contact between the sphere and a simplified model of the cloth. In simulation, we demonstrate successful navigation of a robotic arm through a variety of garments, including an isolated sleeve, a jacket, a shirt, and shorts. Our controller out-performs two baseline controllers: one without haptics and another that was trained based on large forces between the sphere and cloth, but without early termination.Comment: Supplementary video available at https://youtu.be/iHqwZPKVd4A. Related publications http://www.cc.gatech.edu/~karenliu/Robotic_dressing.htm

Quantitative hierarchical representation and comparison of hand grasps from electromyography and kinematic data

Motivation: Modeling human grasping and hand movements is important for robotics, prosthetics and rehabilitation. Several qualitative taxonomies of hand grasps have been proposed in scientific literature. However it is not clear how well they correspond to subjects movements. Objective: In this work we quantitatively analyze the similarity between hand movements in 40 subjects using different features. Methods: Publicly available data from 40 healthy subjects were used for this study. The data include electromyography and kinematic data recorded while the subjects perform 20 hand grasps. The kinematic and myoelectric signal was windowed and several signal features were extracted. Then, for each subject, a set of hierarchical trees was computed for the hand grasps. The obtained results were compared in order to evaluate differences between features and different subjects. Results: The comparison of the signal feature taxonomies revealed a relation among the same subject. The comparison of the subject taxonomies highlighted also a similarity shared between subjects except for rare cases. Conclusions: The results suggest that quantitative hierarchical representations of hand movements can be performed with the proposed approach and the results from different subjects and features can be compared. The presented approach suggests a way to perform a systematic analysis of hand movements and to create a quantitative taxonomy of hand movements

Sampling-Based Methods for Factored Task and Motion Planning

This paper presents a general-purpose formulation of a large class of discrete-time planning problems, with hybrid state and control-spaces, as factored transition systems. Factoring allows state transitions to be described as the intersection of several constraints each affecting a subset of the state and control variables. Robotic manipulation problems with many movable objects involve constraints that only affect several variables at a time and therefore exhibit large amounts of factoring. We develop a theoretical framework for solving factored transition systems with sampling-based algorithms. The framework characterizes conditions on the submanifold in which solutions lie, leading to a characterization of robust feasibility that incorporates dimensionality-reducing constraints. It then connects those conditions to corresponding conditional samplers that can be composed to produce values on this submanifold. We present two domain-independent, probabilistically complete planning algorithms that take, as input, a set of conditional samplers. We demonstrate the empirical efficiency of these algorithms on a set of challenging task and motion planning problems involving picking, placing, and pushing

Simulating Adaptive Human Bipedal Locomotion Based on Phase Resetting Using Foot-Contact Information

Humans generate bipedal walking by cooperatively manipulating their complicated and redundant musculoskeletal systems to produce adaptive behaviors in diverse environments. To elucidate the mechanisms that generate adaptive human bipedal locomotion, we conduct numerical simulations based on a musculoskeletal model and a locomotor controller constructed from anatomical and physiological findings. In particular, we focus on the adaptive mechanism using phase resetting based on the foot-contact information that modulates the walking behavior. For that purpose, we first reconstruct walking behavior from the measured kinematic data. Next, we examine the roles of phase resetting on the generation of stable locomotion by disturbing the walking model. Our results indicate that phase resetting increases the robustness of the walking behavior against perturbations, suggesting that this mechanism contributes to the generation of adaptive human bipedal locomotion

Real-time, interactive, visually updated simulator system for telepresence

Time delays and limited sensory feedback of remote telerobotic systems tend to disorient teleoperators and dramatically decrease the operator's performance. To remove the effects of time delays, key components were designed and developed of a prototype forward simulation subsystem, the Global-Local Environment Telerobotic Simulator (GLETS) that buffers the operator from the remote task. GLETS totally immerses an operator in a real-time, interactive, simulated, visually updated artificial environment of the remote telerobotic site. Using GLETS, the operator will, in effect, enter into a telerobotic virtual reality and can easily form a gestalt of the virtual 'local site' that matches the operator's normal interactions with the remote site. In addition to use in space based telerobotics, GLETS, due to its extendable architecture, can also be used in other teleoperational environments such as toxic material handling, construction, and undersea exploration