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.
Wang, Z., Boularias, A., Mülling, K., Schölkopf, B., Peters, J.
Anticipatory Action Selection for Human-Robot Table Tennis
Artificial Intelligence, 247, pages: 399-414, 2017, Special Issue on AI and Robotics (article)
Zhang, K., Wang, Z., Zhang, J., Schölkopf, B.
On estimation of functional causal models: General results and application to post-nonlinear causal model
ACM Transactions on Intelligent Systems and Technologies, 7(2):article no. 13, January 2016 (article)
Zhang, K., Schölkopf, B., Muandet, K., Wang, Z., Zhou, Z., Persello, C.
Single-Source Domain Adaptation with Target and Conditional Shift
In Regularization, Optimization, Kernels, and Support Vector Machines, pages: 427-456, 19, Chapman & Hall/CRC Machine Learning & Pattern Recognition, (Editors: Suykens, J. A. K., Signoretto, M. and Argyriou, A.), Chapman and Hall/CRC, Boca Raton, USA, 2014 (inbook)
Zhang, K., Schölkopf, B., Muandet, K., Wang, Z.
Domain adaptation under Target and Conditional Shift
In Proceedings of the 30th International Conference on Machine Learning, W&CP 28 (3), pages: 819–827, (Editors: S Dasgupta and D McAllester), JMLR, ICML, 2013 (inproceedings)
Zhang, K., Wang, Z., Schölkopf, B.
On estimation of functional causal models: Post-nonlinear causal model as an example
In First IEEE ICDM workshop on causal discovery , 2013, Held in conjunction with the 12th IEEE International Conference on Data Mining (ICDM 2013) (inproceedings)
Wang, Z., Mülling, K., Deisenroth, M., Ben Amor, H., Vogt, D., Schölkopf, B., Peters, J.
Probabilistic movement modeling for intention inference in human-robot interaction
International Journal of Robotics Research, 32(7):841-858, 2013 (article)
Wang, Z.
Intention Inference and Decision Making with Hierarchical Gaussian Process Dynamics Models
Technical University Darmstadt, Germany, 2013 (phdthesis)
Wang, Z., Deisenroth, M., Ben Amor, H., Vogt, D., Schölkopf, B., Peters, J.
Probabilistic Modeling of Human Movements for Intention Inference
In Proceedings of Robotics: Science and Systems VIII, pages: 8, R:SS, 2012 (inproceedings)
Wang, Z., Lampert, C., Mülling, K., Schölkopf, B., Peters, J.
Learning anticipation policies for robot table tennis
In pages: 332-337 , (Editors: NM Amato), IEEE, Piscataway, NJ, USA, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 2011 (inproceedings)
Wang, Z., Boularias, A., Mülling, K., Peters, J.
Balancing Safety and Exploitability in Opponent Modeling
In pages: 1515-1520, (Editors: Burgard, W. , D. Roth), AAAI Press, Menlo Park, CA, USA, Twenty-Fifth AAAI Conference on Artificial Intelligence (AAAI), August 2011 (inproceedings)
Peters, J., Kober, J., Mülling, K., Krömer, O., Nguyen-Tuong, D., Wang, Z., Rodriguez Gomez, M., Grosse-Wentrup, M.
Learning as a key ability for Human-Friendly Robots
In pages: 1-2, 3rd Workshop for Young Researchers on Human-Friendly Robotics (HFR), October 2010 (inproceedings)