Eric Huber

Superpositioning of behaviors learned through teleoperation

This paper reports that the superposition of a small set of behaviors, learned via teleoperation, can lead to robust completion of an articulated reach-and-grasp task. The results support the hypothesis that a robot can learn to interact purposefully with its environment through a developmental acquisition of sensory-motor coordination. Teleoperation can bootstrap the process by enabling the robot to observe its own sensory responses to actions that lead to specific outcomes within an environment. It is shown that a reach-and-grasp task, learned by an articulated robot through a small number of teleoperated trials, can be performed autonomously with success in the face of significant variations in the environment and perturbations of the goal. In particular, teleoperation of the robot to reach and grasp an object at nine different locations in its workspace enabled robust autonomous performance of the task anywhere within the workspace. Superpositioning was performed using the Verbs and Adverbs algorithm that was developed originally for the graphical animation of articulated characters. The work was performed on Robonaut, the NASA space-capable humanoid at Johnson Space Center, Houston, TX.

Robonaut Mobile Autonomy: Initial Experiments

A mobile version of the NASA/DARPA Robonaut humanoid recently completed initial autonomy trials working directly with humans in cluttered environments. This compact robot combines the upper body of the Robonaut system with a Segway ™ Robotic Mobility Platform yielding a dexterous, maneuverable humanoid ideal for interacting with human co-workers in a range of environments. This system uses stereovision to locate human teammates and tools and a navigation system that uses laser range and vision data to follow humans while avoiding obstacles. Tactile sensors provide information to grasping algorithms for efficient tool exchanges. The autonomous architecture utilizes these pre-programmed skills to form complex behaviors. The initial behavior demonstrates a robust capability to assist a human by acquiring a tool from a remotely located individual and then following the human in a cluttered environment with the tool for future use.

Using a hybrid of silhouette and range templates for real-time pose estimation

Pose estimation is a fundamental problem in machine vision. Silhouette and 3D point matching are two of the many popular methods for attacking this problem. The appearance of a silhouette is highly sensitive to changes in the objects planar (WRT the object plane) location and orientation. Therefore, silhouette matching can be used to accurately measure 3 of the 6 pose parameters. The appearance of a silhouette is less sensitive to DOFs that produce out-of-plane motion and so it enables only rough measurement of these 3 pose parameters. 3D point matching, which employs range data, can be used to accurately determine the 3 out-of-plane pose parameters. However, to recognize specific 3d points, one must typically make strong assumptions about the types of features present on an objects surface. Our goal is to solve the more general pose problem, where specific types of features cannot be relied upon because they might not be present. This paper first introduces a novel approach to silhouette matching which employs binary range maps and statistically generated templates. It then describes a hybrid method for template-based pose estimation that leverages silhouette matching for 3 of 6 pose parameters. The remaining 3 parameters are determined using range measurements that do not require the presence of specific features or artifacts. This approach provides a high degree of precision in all 6 DOFs, yet its computational efficiency enables real-time performance. Results from dexterous robot (Robonaut) experiments, using this pose algorithm, are discussed.

Real-time tracking of people using stereo and motion

Depth and motion measurements from images have a variety of industrial and non-industrial applications. Underlying these two measurement modalities is the ability to accurately and reliably correlate portions of images separated in space, as in the case of a stereo pair, or time, as in the case of a motion sequence. We have developed a unique correlation algorithm as well as special hardware accelerators to allow it to operate at video frame rate. The ability to make measurements in real time is invaluable in observing algorithm performance on dynamic scenes; real-time feedback has also allowed us to apply these low-level measurements in unique ways to several perceptual tasks. In this paper, we describe our progress in the areas of figure-ground separation and active object tracking using our correlation techniques. We illustrate the use of these capabilities to drive an active camera head and a robot vehicle in person-following demonstrations.

Confidence assessment for stereo depth mapping using a PRISM-3

Computationally economical techniques for employing confidence measures to generate depth maps are presented in this paper. The NASA-JSC PRISM system has been successfully applied to experiments in manipulation (EVA Helper Retriever) and mobility (Mobile Robot Lab). Results from these experiments and future plans are also presented.

USEFUL INTERACTIONS BETWEEN HUMAN AND ROBOTIC AGENTS PERFORMING A COOPERATIVE ASSEMBLY TASK

NASAs Human Space Flight program depends heavily on spacewalks performed by human astronauts. These Extra -Vehicular Activities (EVAs) are risk y, expensive and complex. In collaboration with the Defense Advanced Research Projects Agency (DARPA), NASA is developing a robotic astronauts assistant called Robonaut that can boost EVA productivity and help conserve human EVA hours. Robonaut is a teleo perated, anthropomorphic robot equipped with human -like dexterous manipulation and zero -G locomotion capabilities. An experiment is conducted to study human -robot interaction in the context of a simplified EVA assembly task in which Robonaut works side -by -side with a human subject. Agent interactions are carefully controlled to examine the effects of increasingly sophisticated forms of communication on overall task performance. Performance metrics include task completion time and interaction forces and torq ues.