While probabilistic models are useful to classify and infer trajectories, a common problem is that their construction usually requires the time alignment of training data such that spatial correlations can be properly captured. In a single-agent robot case, this is usually not a problem as robots move in a controlled manner. However, when the human is the agent that provides observations, repeatability and temporal consistency becomes an issue as it is not trivial to align partially observed trajectories of the observed human with a probabilistic model, particularly online and under occlusions. Since the goal of the human movement is unknown, it is difficult to estimate the progress or phase of the movement. We approach this problem by testing many sampled hypotheses of his/her movement speed, online. This usually allows us to recognize the human action and generate the appropriate robot trajectory. The video shows some of the benefits of estimating phases for faster robot reactions. It also shows the interesting case when the robot tries to predict the human motion too early, therefore leading to some awkward/erroneous coordination.
- Maeda, G.; Ewerton, M; Neumann, G.; Lioutikov, R.; Peters, J. “Phase Estimation for Fast Action Recognition and Trajectory Generation in Human-Robot Collaboration”, Accepted. International Journal of Robotics Research (IJRR). [pdf][BibTeX]
- Maeda, G.; Neumann, G.; Ewerton, M.; Lioutikov, R.; Peters, J. (2015). “A Probabilistic Framework for Semi-Autonomous Robots Based on Interaction Primitives with Phase Estimation”, International Symposium of Robotics Research (ISRR). [pdf][BiBTeX]
- Ewerton, M.; Maeda, G.; Peters, J.; Neumann, G. (2015). “Learning Motor Skills from Partially Observed Movements Executed at Different Speeds”, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 456–463. [pdf][BibTeX]