Assistive robotic arms are increasingly enabling users with upper extremity disabilities to perform activities of daily living on their own. However, the increased capability and dexterity of the arms also makes them harder to control with simple, low-dimensional interfaces like joysticks and sip-and-puff interfaces. A common technique to control a high-dimensional system like an arm with a low-dimensional input like a joystick is through switching between multiple control modes. However, our interviews with daily users of the Kinova JACO arm identified mode switching as a key problem, both in terms of time and cognitive load. We further confirmed objectively that mode switching consumes about 17.4% of execution time even for able-bodied users controlling the JACO. Our key insight is that using even a simple model of mode switching, like time optimality, and a simple intervention, like automatically switching modes, significantly improves user satisfaction.

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Abstract-The regularity of everyday tasks enables us to reuse existing solutions for task variations. For instance, most door-handles require the same basic skill (reach, grasp, turn, pull), but small adaptations of the basic skill are required to adapt to the variations that exist (e.g. levers vs. knobs). We introduce the algorithm "Simultaneous On-line Discovery and Improvement of Robotic Skills" (SODIRS) that is able to autonomously discover and optimize skill options for such task variations. We formalize the problem in a reinforcement learning context, and use the PI BB algorithm [2] to continually optimize skills with respect to a cost function. SODIRS discovers new subskills, or "skill options", by clustering the costs of trials, and determining whether perceptual features are able to predict which cluster a trial will belong to. This enables SODIRS to build a decision tree, in which the leaves contain skill options for task variations. We demonstrate SODIRS' performance in simulation, as well as on a Meka humanoid robot performing the ball-in-cup task.

The AAAI 2016 Fall Symposium Series was held Thursday through Saturday, November 17–19, at the Westin Arlington Gateway in Arlington, Virginia adjacent to Washington, DC. The titles of the six symposia were Accelerating Science: A Grand Challenge for AI; Artificial Intelligence for Human-Robot Interaction, Cognitive Assistance in Government and Public Sector Applications, Cross-Disciplinary Challenges for Autonomous Systems, Privacy and Language Technologies, Shared Autonomy in Research and Practice. The highlights of each (except Acceleration Science) symposium are presented in this report.