Navigation and Obstacle Avoidance Testbed


Open World Assistive Grasping Using Laser Selection


University of Massachusetts Lowell
http://robotics.cs.uml.edu/

Andreas ten Pas, Marcus Gualtieri, and Robert Platt
Northeastern University
http://www.ccs.neu.edu/research/helpinghands/

Many people with motor disabilities are unable to complete activities of daily living (ADLs) without assistance. This video shows a robotic system developed to provide mobile grasping assistance for ADLs. The system is comprised of a robot arm from a Rethink Robotics Baxter robot mounted to an assistive mobility device, a control system for that arm, and a user interface with a variety of access methods for selecting desired objects. The system uses grasp detection to allow previously unseen objects to be picked up by the system. The grasp detection algorithms also allow for objects to be grasped in cluttered environments. With evaluations with able-bodied users, we achieved an object selection success rate of 88% and a grasp detection success rate of 90% in a non-mobile scenario, and success rates of 89% and 72% in a mobile scenario.

This work has been supported in part by the National Science Foundation through IIS-1426968 and IIS-1427081, NASA through NNX16AC48A and NNX13AQ85G, and ONR through N000141410047.

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