Recent advances in sensors and algorithms allow for robots with improved perception abilities. However, effective perception alone may not be sufficient for human-robot interaction, since the robot's reaction should depend on understanding the human's intention. Hence, my research interests lie in the strategic level of human-robot interaction, which serves as a bridge between perception of human action and planning for reaction. On one side, the robot needs to infer the underlying intention of humans. On the other side, efficient planning for reaction can be achieved by utilizing motor skills with reactive policies learned to choose the right skill at the right time.
I have been developing and implementing machine learning algorithms for intention inference and learning reactive policies. I have chosen robot table tennis as a benchmark, as it is a sufficiently complex scenario for evaluation while intuition still allows interpreting the results. We have achieved promising experimental results, which exhibit their potentials in many other human-robot interaction scenarios.
See http://robot-learning.de/Team/ZhikunWang for more information.
, , , , , and (2012) Probabilistic movement modeling for intention inference in human-robot interaction
International Journal of Robotics Research . accepted|
Conference papers (5):
, , and (2013) Domain adaptation under Target and Conditional Shift
30th International Conference on Machine Learning (ICML 2013).
, , , , and (2012) Probabilistic Modeling of Human Movements for Intention Inference
Robotics: Science and Systems (R:SS 2012), 8 pages.
, , , and (2011) Learning anticipation policies for robot table tennis
(Ed) NM Amato, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011), IEEE, Piscataway, NJ, USA, 332-337.
, , and (2011) Balancing Safety and Exploitability in Opponent Modeling
(Ed) Burgard, W. , D. Roth, Twenty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2011), AAAI Press, Menlo Park, CA, USA, 1515-1520.
, , , , , , and (2010) Learning as a key ability for Human-Friendly Robots
3rd Workshop for Young Researchers on Human-Friendly Robotics (HFR 2010), 1-2.