Abstract
In the past few years a new scenario for robot-based applications has emerged. Service and mobile robots have opened new market niches. Also, new frameworks for shop-floor robot applications have been developed. In all these contexts, robots are requested to perform tasks within open-ended conditions, possibly dynamically varying. These new requirements ask also for a change of paradigm in the design of robots: on-line and safe feedback motion control becomes the core of modern robot systems. Future robots will learn autonomously, interact safely and possess qualities like self-maintenance. Attaining these features would have been relatively easy if a complete model of the environment was available, and if the robot actuators could execute motion commands perfectly relative to this model. Unfortunately, a complete world model is not available and robots have to plan and execute the tasks in the presence of environmental uncertainties which makes sensing an important component of new generation robots. For this reason, today's new generation robots are equipped with more and more sensing components, and consequently they are ready to actively deal with the high complexity of the real world. Complex sensorimotor tasks such as exploration require coordination between the motor system and the sensory feedback. For robot control purposes, sensory feedback should be adequately organized in terms of relevant features and the associated data representation. In this paper, we propose an overall functional picture linking sensing to action in closed-loop sensorimotor control of robots for touch (hands, fingers). Basic qualities of haptic perception in humans inspire the models and categories comprising the proposed classification. The objective is to provide a reasoned, principled perspective on the connections between different taxonomies used in the Robotics and human haptic literature. The specific case of active exploration is chosen to ground interesting use cases. Two reasons motivate this choice. First, in the literature on haptics, exploration has been treated only to a limited extent compared to grasping and manipulation. Second, exploration involves specific robot behaviors that exploit distributed and heterogeneous sensory data.
Highlights
There is a compelling case for using principles of human haptic perception—active touch—to inspire the development of robot haptic systems
While completely handling open-ended active behaviors is not mature as a feature yet because, conceptually, Artificial Intelligence is bound to the concept of a closed-world model, first attempts toward specific active approaches involving well-defined tasks can be found in the recent literature pertaining to the niche of Cognitive Robotics
It is worth noting that this approach is not the gold standard; in contrast, active exploration is still cutting-edge and experimental in Robotics research, and underappreciated
Summary
There is a compelling case for using principles of human haptic perception—active touch—to inspire the development of robot haptic systems. Prescott et al (2011) describe work by Kappers (2011) showing that surface curvature is judged on the basis of a reduced “dataset” from what is available in principle at the sensors They note that such dimension reduction has been proposed by Hayward (2011) as reflecting a general principle, whereby the brain makes “simplifying assumptions” that are necessary to manage the complexity of the totality of information that can potentially be acquired by the haptic sensory system (what Hayward calls the “plenhaptic function”; analogous to the plenoptic function, or light field, in optics). Motor control and sensory input are interlinked in human haptic sensing: purposive movements provide information to build internal representations of properties of the world and guide future movements, in a closely coupled manner. The predicted state of the limb can be combined with sensory feedback to estimate its state optimally, given uncertainty in all estimates, and despite delayed sensory feedback
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