Siddhartha Banerjee
ABSTRACT
Robust autonomy can be achieved with learning frameworks that refine robot operating procedures through guidance from human domain experts. This work explores three capabilities required to implement efficient learning for robust autonomy: (1) identifying when to garner human input during task execution, (2) using active learning to curate what guidance is received, and (3) evaluating the tradeoff between operator availability and guidance fidelity when deciding who to enlist for guidance. We present results from completed work on interruptibility classification of collocated people that can be used to help in evaluating the tradeoff in (3).
- Siddhartha Banerjee and Sonia Chernova. 2017. Temporal Models for Robot Classification of Human Interruptibility. In AAMAS. 1350--1359. Google Scholar
Digital Library
- Sonia Chernova and Andrea L. Thomaz. 2014. Robot Learning from Human Teachers. Vol. 8. 1--121 pages. Google ScholarDigital Library
- Sonia Chernova and Manuela Veloso. 2007. Confidence-based policy learning from demonstration using Gaussian mixture models. In AAMAS. Google ScholarDigital Library
- Vivian Chu, Tesca Fitzgerald, and Andrea L Thomaz. 2016. Learning object affordances by leveraging the combination of human-guidance and self-exploration. In HRI. IEEE Press, 221--228. Google ScholarDigital Library
- Sébastien Forestier, Yoan Mollard, and Pierre-Yves Oudeyer. 2017. Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning. (aug 2017). arXiv:1708.02190Google Scholar
- Constantine Lignos, Vasumathi Raman, Cameron Finucane, Mitchell Marcus, and Hadas Kress-Gazit. 2014. Provably correct reactive control from natural language. Autonomous Robots 38, 1 (2014), 89--105. Google ScholarDigital Library
- C. Mollaret, A.A. Mekonnen, F. Lerasle, I. Ferrané, J. Pinquier, B. Boudet, and P. Rumeau. 2016. A multi-modal perception based assistive robotic system for the elderly. Computer Vision and Image Understanding (mar 2016). Google ScholarDigital Library
- Louis-Philippe Morency, Ariadna Quattoni, and Trevor Darrell. 2007. LatentDynamic Discriminative Models for Continuous Gesture Recognition. In CVPR. IEEE, 1--8.Google Scholar
- Liam D. Turner, Stuart M. Allen, and Roger M. Whitaker. 2015. Interruptibility prediction for ubiquitous systems. In UbiComp. ACM Press, 801--812. Google ScholarDigital Library
Index Terms
- Efficient Human-Robot Interaction for Robust Autonomy in Task Execution
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