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

Modeling the light curve of the transient SCP06F6

We consider simple models based on core collapse or pair-formation supernovae to account for the light curve of the transient SCP06F6. A radioactive decay diffusion model provides estimates of the mass of the required radioactive nickel and the ejecta as functions of the unknown redshift. An opacity change such as by dust formation or a recombination front may account for the rapid decline from maximum. We particularly investigate two specific redshifts: $z=0.143$, for which Gaensicke et al. (2008) have proposed that the unidentified broad absorption features in the spectrum of SCP06F6 are C$_{2}$ Swan bands, and $z=0.57$ based on a crude agreement with the Ca H&K and UV iron-peak absorption features that are characteristic of supernovae of various types. The ejected masses and kinetic energies are smaller for a more tightly constrained model invoking envelope recombination. We also discuss the possibilities of circumstellar matter (CSM) shell diffusion and shock interaction models. In general, optically-thick CSM diffusion models can fit the data with the underlying energy coming from an energetic buried supernova. Models in which the CSM is of lower density so that the shock energy is both rapidly thermalized and radiated tend not to be self-consistent. We suggest that a model of SCP06F6 worth futher exploration is one in which the redshift is $\sim$ 0.57, the spectral features are Ca and iron peak elements, and the light curve is powered by the diffusive release of a substantial amount of energy from nickel decay or from an energetic supernova buried in the ejecta of an LBV-like event.Comment: 27 pages, 6 figure

High-Dimensional Motion Planning and Learning Under Uncertain Conditions

Many existing path planning methods do not adequately account for uncertainty. Without uncertainty these existing techniques work well, but in real world environments they struggle due to inaccurate sensor models, arbitrarily moving obstacles, and uncertain action consequences. For example, picking up and storing childrens toys is a simple task for humans. Yet, for a robotic household robot the task can be daunting. The room must be modeled with sensors, which may or may not detect all the strewn toys. The robot must be able to detect and avoid the child who may be moving the very toys that the robot is tasked with cleaning. Finally, if the robot missteps and places a foot on a toy, it must be able to compensate for the unexpected consequences of its actions. This example demonstrates that even simple human tasks are fraught with uncertainties that must be accounted for in robotic path planning algorithms. This work presents the first steps towards migrating sampling-based path planning methods to real world environments by addressing three different types of uncertainty: (1) model uncertainty, (2) spatio-temporal obstacle uncertainty (moving obstacles) and (3) action consequence uncertainty. Uncertainty is encoded directly into path planning through a data structure in order to successfully and efficiently identify safe robot paths in sensed environments with noise. This encoding produces comparable clearance paths to other planning methods which are a known for high clearance, but at an order of magnitude less computational cost. It also shows that formal control theory methods combined with path planning provides a technique that has a 95% collision-free navigation rate with 300 moving obstacles. Finally, it demonstrates that reinforcement learning can be combined with planning data structures to autonomously learn motion controls of a seven degree of freedom robot despite a low computational cost despite the number of dimensions

Refining Obstacle Perception Safety Zones via Maneuver-Based Decomposition

A critical task for developing safe autonomous driving stacks is to determine whether an obstacle is safety-critical, i.e., poses an imminent threat to the autonomous vehicle. Our previous work showed that Hamilton Jacobi reachability theory can be applied to compute interaction-dynamics-aware perception safety zones that better inform an ego vehicle's perception module which obstacles are considered safety-critical. For completeness, these zones are typically larger than absolutely necessary, forcing the perception module to pay attention to a larger collection of objects for the sake of conservatism. As an improvement, we propose a maneuver-based decomposition of our safety zones that leverages information about the ego maneuver to reduce the zone volume. In particular, we propose a "temporal convolution" operation that produces safety zones for specific ego maneuvers, thus limiting the ego's behavior to reduce the size of the safety zones. We show with numerical experiments that maneuver-based zones are significantly smaller (up to 76% size reduction) than the baseline while maintaining completeness.Comment: * indicates equal contribution. Accepted into the IEEE Intelligent Vehicles Symposium 202