Robotic learning from distributed sensory sources

Recent work toward the development of low-complexity, sensor-based inferencing methods to serve as the initial links of incremental robotic learning systems is described. A multimodal learning approach is proposed in which distributed sensory sources are used to both trigger the observation of and perceive relevant learning instances in a human-robot synergistic framework. Three components of the incremental learning system for the CESARm advanced manipulator testbed are presented that encompass the learning of objects and work area characteristics through the triggering of attention and rote learning, the learning of elemental manipulation tasks by observation of human actions, and the self-assessment of acquired skills and learned knowledge through task performance evaluation. Feasibility experiments with each of these three learning methodologies are presented, and some results are discussed