Abstract
Robots should be capable of interacting in a cooperative and adaptive manner with their human counterparts in open-ended tasks that can change in real-time. An important aspect of the robot behavior will be the ability to acquire new knowledge of the cooperative tasks by observing and interacting with humans. The current research addresses this challenge. We present results from a cooperative human-robot interaction system that has been specifically developed for portability between different humanoid platforms, by abstraction layers at the perceptual and motor interfaces. In the perceptual domain, the resulting system is demonstrated to learn to recognize objects and to recognize actions as sequences of perceptual primitives, and to transfer this learning, and recognition, between different robotic platforms. For execution, composite actions and plans are shown to be learnt on one robot and executed successfully on a different one. Most importantly, the system provides the ability to link actions into shared plans, that form the basis of human-robot cooperation, applying principles from human cognitive development to the domain of robot cognitive systems.
Highlights
For embodied agents that perceive and act in the world, there is a strong coupling or symmetry between perception and execution which is constructed around the notion of goal directed action
Within a sensorimotor architecture a number of benefits derive from such a format, including the direct relation between action perception and execution that can provide the basis for imitation
The current research extends our previous work on the learning of composite actions by exploiting this proposed relation between action execution and perception
Summary
For embodied agents that perceive and act in the world, there is a strong coupling or symmetry between perception and execution which is constructed around the notion of goal directed action. Our novel contribution to this domain is the encoding of action in terms of perceptual state changes and composed motor primitives that can achieve these state changes, in a manner that allows the robot to learn new actions as perception – execution pairs, and use this knowledge to perceive and imitate. These actions can take several arguments, e.g. AGENT put the OBJECT on the RECIPIENT. In our long-term research program, this provides the basis for learning to perform joint cooperative tasks purely through observation
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