Incremental learning of context-dependent dynamic internal models for robot control

Accurate dynamic models can be very difficult to compute analytically for complex robots; moreover, using a precomputed fixed model does not allow to cope with unexpected changes in the system. An interesting alternative solution is to learn such models from data, and keep them up-to-date through online adaptation. In this paper we consider the problem of learning the robot inverse dynamic model under dynamically varying contexts: the robot learns incrementally and autonomously the model under different conditions, represented by the manipulation of objects of different weights, that change the dynamics of the system. The inverse dynamic mapping is modeled as a multi-valued function, in which different outputs for the same input query are related to different dynamic contexts (i.e. different manipulated objects). The mapping is estimated using IMLE, a recent online learning algorithm for multi-valued regression, and used for Computed Torque control. No information is given about the context switch during either learning or control, nor any assumption is made about the kind of variation in the dynamics imposed by a new contexts. Experimental results with the iCub humanoid robot are provided.