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Nakul Gopalan, Nina M Moorman, Manisha Natarajan, Matthew Gombolay (Georgia Institute of Technology) |
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| Paper #028 |
| Session 4. Short talks |
Learning from demonstration (LfD) seeks to democratize robotics by enabling non-experts to intuitively program robots to perform novel skills through human task demonstration. Yet, LfD is challenging under a task and motion planning setting which requires hierarchical abstractions. Prior work has studied mechanisms for eliciting demonstrations that include hierarchical specifications of task and motion, via keyframes [1] or hierarchical task network specifications [2]. However, such prior works have not examined whether non-roboticist end-users are capable of providing such hierarchical demonstrations without explicit training from a roboticist showing how to teach each task [3]. To address the limitations and assumptions of prior work, we conduct two novel human-subjects experiments to answer (1) what are the necessary conditions to teach users through hierarchy and task abstractions and (2) what instructional information or feedback is required to support users to learn to program robots effectively to solve novel tasks. Our first experiment shows that fewer than half (35.71%) of our subjects provide demonstrations with sub-task abstractions when not primed. Our second experiment demonstrates that users fail to teach the robot correctly when not shown a video demonstration of an expert’s teaching strategy for the exact task that the subject is training. Not even showing the video of an analogue task was sufficient. These experiments reveal the need for fundamentally different approaches in LfD which can allow end-users to teach generalizable long-horizon tasks to robots without the need to be coached by experts at every step.
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