Abstract
Evolved living beings can anticipate the consequences of their actions in complex multilevel dynamic situations. This ability relies on abstracting the meaning of an action. The underlying brain mechanisms of such semantic processing of information are poorly understood. Here we show how our novel concept, known as time compaction, provides a natural way of representing semantic knowledge of actions in time-changing situations. As a testbed, we model a fencing scenario with a subject deciding between attack and defense strategies. The semantic content of each action in terms of lethality, versatility, and imminence is then structured as a spatial (static) map representing a particular fencing (dynamic) situation. The model allows deploying a variety of cognitive strategies in a fast and reliable way. We validate the approach in virtual reality and by using a real humanoid robot.
Highlights
Efficient object manipulation is simultaneously one of the most apparent features of humans’ daily life and one of the most challenging skills that modern humanoid robots largely lack
We develop a novel approach to constructing behaviors based on the semantic description of motor-motifs emerging from generalized cognitive map (GCM)
Let us first consider how a GCM can be constructed for a novel dynamic situation
Summary
Efficient object manipulation is simultaneously one of the most apparent features of humans’ daily life and one of the most challenging skills that modern humanoid robots largely lack (see e.g., Calvo et al, 2018b; Billard and Kragicet, 2019 and references therein). Children spent years to acquire adult-equivalent skills in manipulation (Thibaut and Toussaint, 2010). Such simple-but-difficult tasks possess vast intrinsic complexity, which impedes robots to mimic even basic human abilities in real-life scenarios. Modern robots are capable of manipulating objects in repetitive and controlled conditions, e.g., in industrial assembly setups. In such tailor-made scenarios, a purely programmatic approach to the problem of limb movement works rather well (Choset et al, 2005; Patel and Shadpey, 2005). There is a growing body of approaches addressing the problems of a robust prediction of trajectories of objects, fast calculation of feasible postures and movements of limbs through, e.g., splines, etc. (Riley and Atkeson, 2002; Aleotti and Caselli, 2006; Xiao et al, 2016)
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