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Human touch makes robots smarter: On Learning Context-Driven User Preferences

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Published on Oct 31, 2013

More details can be found at http://pr.cs.cornell.edu/coactive
This research was done at the Cornell University Personal Robotics Lab by PhD student Ashesh Jain and Prof. Ashutosh Saxena.

In this video, the robot does not know how to move a knife safely and learns through Interactive zero-G and tablet touch user interaction on Baxter for grocery checkout application. With high degree-of-freedom manipulators, one needs to find a trajectory that is not only valid from a geometric point-of-view (i.e., feasible and obstacle-free, the criterion that most existing planners focus on), but also satisfy the user's preferences. In this work, we propose an algorithm to learn such preferences via eliciting online feedback from the user, which does not need to be an optimal demonstrations.

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