Learning Strategy in Time-to-Contact Estimation of Falling Objects
Hiroyuki Kambara*1, Keiichi Ohishi*2, and Yasuharu Koike*1,*3,*4
*1Precision and Intelligence Lab., Tokyo Institute of Technology, R2-15, 4259 Nagatsuda, Midori-ku, Yokohama 226-8503, Japan
*2Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Japan
*3Solutions Research Lab., Tokyo Institute of Technology, Japan
*4CREST, JST, Japan
The ability to estimate the time that remains before contact (Time-To-Contact or TTC) of a falling object is critical in daily life. In this paper, we investigated how the Central Nervous System (CNS) becomes able to estimate the TTC of a ball falling at various accelerations. According to experiments on the human ability to catch a ball falling at various accelerations, we assumed that the CNS can hold multiple TTC estimators each of which is trained for a different acceleration, and one of them is adopted for TTC estimation in a ball-catching trial. Here we made a hypothesis about how each TTC estimator is trained when there is an estimation error. (1) If the estimation error is small, the TTC estimator adopted in the trial is recalibrated. (2) If the estimation error is large, a new TTC estimator is created. To test this hypothesis, we conducted two types of ball-catching experiments in a virtual environment where the acceleration of a virtual ball is changed gradually or suddenly in each experiment. The difference in catching performances in the two experiments supported our hypothesis.
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