ABSTRACT
We have developed learning and interaction algorithms to support a human teaching hierarchical task models to a robot using a single demonstration in the context of a mixed-initiative interaction with bi-directional communication. In particular, we have identified and implemented two important heuristics for suggesting task groupings based on the physical structure of the manipulated artifact and on the data flow between tasks. We have evaluated our algorithms with users in a simulated environment and shown both that the overall approach is usable and that the grouping suggestions significantly improve the learning and interaction.
- B. D. Argall, S. Chernova, M. Veloso, and B. Browning. A survey of robot learning from demonstration. Robotics and Autonomous Systems, 57(5):469--483, 2009. Google ScholarDigital Library
- M. Cakmak and A. L. Thomaz. Designing robot learners that ask good questions. In ACM/IEEE International Conference on Human-Robot Interaction, pages 17--24. ACM, 2012. Google ScholarDigital Library
- S. Chernova and M. Veloso. Interactive policy learning through confidence-based autonomy. Journal of Artificial Intelligence Research, 34(1):1, 2009. Google ScholarCross Ref
- A. Garland, K. Ryall, and C. Rich. Learning hierarchical task models by defining and refining examples. In International Conference on Knowledge Capture, pages 44--51, 2001. Google ScholarDigital Library
- B. J. Grosz and C. L. Sidner. Attention, intentions, and the structure of discourse. Comput. Linguist., 12(3):175--204, July 1986. Google ScholarDigital Library
- B. Hayes. Social hierarchical learning. In Proceedings of the 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI 2013) Pioneers Workshop, 2013.Google Scholar
- B. Hayes and B. Scassellati. Discovering task constraints through observation and active learning. In IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014.Google ScholarCross Ref
- S. B. Huffman and J. E. Laird. Flexibly instructable agents. Journal of Artificial Intelligence Research, 3:271--324, 1995. Google ScholarDigital Library
- S. Mohan and J. E. Laird. Towards situated, interactive, instructable agents in a cognitive architecture. In AAAI Fall Symposium Series, 2011.Google Scholar
- A. Mohseni-Kabir, S. Chernova, and C. Rich. Collaborative learning of hierarchical task networks from demonstration and instruction. In RSS Workshop on Human-Robot Collaboration for Industrial Manufacturing, Berkeley, CA, July 2014.Google Scholar
- A. Mohseni-Kabir, C. Rich, and S. Chernova. Learning partial ordering constraints from a single demonstration. In ACM/IEEE International Conference on Human-robot interaction, pages 248--249, 2014. Google ScholarDigital Library
- M. N. Nicolescu and M. J. Mataric. Natural methods for robot task learning: Instructive demonstrations, generalization and practice. In AAMAS, pages 241--248, 2003. Google ScholarDigital Library
- C. Rich. Building task-based user interfaces with ANSI/CEA-2018. IEEE Computer, 42(8):20--27, 2009. Google ScholarDigital Library
- C. Rich and C. L. Sidner. Using collaborative discourse theory to partially automate dialogue tree authoring. In Proc. Int. Conf. on Intelligent Virtual Agents, pages 327--340, Santa Cruz, CA, Sept. 2012. Google ScholarDigital Library
- P. E. Rybski, K. Yoon, J. Stolarz, and M. M. Veloso. Interactive robot task training through dialog and demonstration. In ACM/IEEE Int. Conf. on Human-Robot Interaction, pages 49--56, 2007. Google ScholarDigital Library
- H. Veeraraghavan and M. Veloso. Learning task specific plans through sound and visually interpretable demonstrations. In IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 2599--2604, 2008.Google Scholar
Index Terms
- Interactive Hierarchical Task Learning from a Single Demonstration
Recommendations
Learning from demonstration for semi-autonomous teleoperation
Teleoperation in domains such as deep-sea or space often requires the completion of a set of recurrent tasks. We present a framework that uses a probabilistic approach to learn from demonstration models of manipulation tasks. We show how such a ...
Learning from Demonstration for Autonomous Navigation in Complex Unstructured Terrain
Rough terrain autonomous navigation continues to pose a challenge to the robotics community. Robust navigation by a mobile robot depends not only on the individual performance of perception and planning systems, but on how well these systems are ...
Robot life-long task learning from human demonstrations: a Bayesian approach
Programming a robot to act intelligently is a challenging endeavor beyond the skill level of most people. Trained roboticists generally program robots for a single purpose. Enabling robots to be programmed by non-experts and to perform multiple tasks ...
Comments