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.

The NASA/DARPA Robonaut system is evolving from a purely teleoperator controlled anthropomorphic robot towards a humanoid system with multiple control pathways. Robonaut is a human scale robot designed to approach the dexterity of a space suited astronaut. Under teleoperator control, Robonaut has been able to perform many high payoff tasks indicating that it could significantly reduce the maintenance workload for human's working in space. Throughout its development, Robonaut has been augmented to include new sensors and software resulting in increased skills that allow for more shared control with the teleoperator, and ever increasing levels of autonomy. These skills range from simple compliance control, and short term memory, to, most recently, reflexive grasping and haptic object identification using a custom tactile glove, and real-time visual object tracking.

This paper addresses the problem of automatic skill acquisition by a robot. It reports that six trials of a reach-grasp-release-retract skill are sufficient for learning a canonical description of the task under the following circumstances: The robot is Robonaut, NASA's space-capable, dexterous humanoid. Robonaut was teleoperated by a person using full immersion Virtual Reality technology that transforms the operator's arm and hand motions into those of the robot. The operator's sole source of real-time feedback was visual. During the six trials all of the Robot's sensory inputs and motor control parameters were recorded as time-series. Later the time-series from each trial was partitioned into the same number of episodes as a function of changes in the motor parameter sequence. The episodes were time normalized and averaged across trials The resultant motor parameter sequence and sensor signals were used to control the robot without the teleoperator. The robot was able to perform the task autonomously with robot starting positions and object locations both similar to, and different from the original trials.

Effectively controlling a robot remotely, teleoperation, offers benefits in improving human safety, reducing workload, providing location accessibility, and achieving convenient operation. In previous work, teleoperator task performance has been shown to improve with the addition of force feedback. Here we report on a system providing a human operator with a six-axis kinesthetic and visual force display, its implementation and experimental evaluation. A system design containing both a six-axis force reflective hand controller and a stereo video head-mounted display with 3D force display overlay is presented. The system was utilized to evaluate the effects of force display augmentation and substitution by controlling a dexterous anthropomorphic robot called "Robonaut" in the performance of a typical space teleoperation drill task. The human test subject performance under four different force feedback scenarios, no force display, visual force display, kinesthetic force display and bisensory force display, is measured. Experimental results indicate a noticeably lower cumulative force, torque, and variance, lower peak force/torque, yet unchanged task completion times under the presence of visual or kinesthetic force display.