Prediction-for-CompAction: navigation in social environments using generalized cognitive maps



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Villacorta Atienza, Jose A. and Calvo, Carlos and Makarov, Valeri A. (2015) Prediction-for-CompAction: navigation in social environments using generalized cognitive maps. Biological Cybernetics, 109 (3). pp. 307-320. ISSN 0340-1200

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The ultimate navigation efficiency of mobile robots in human environments will depend on how we will appraise them: merely as impersonal machines or as human-like agents. In the latter case, an agent may take advantage of the cooperative collision avoidance, given that it possesses recursive cognition, i.e., the agent's decisions depend on the decisions made by humans that in turn depend on the agent's decisions. To deal with this high-level cognitive skill, we propose a neural network architecture implementing Prediction-for-CompAction paradigm. The network predicts possible human-agent collisions and compacts the time dimension by projecting a given dynamic situation into a static map. Thereby emerging compact cognitive map can be readily used as a "dynamic GPS" for planning actions or mental evaluation of the convenience of cooperation in a given context. We provide numerical evidence that cooperation yields additional room for more efficient navigation in cluttered pedestrian flows, and the agent can choose path to the target significantly shorter than a robot treated by humans as a functional machine. Moreover, the navigation safety, i.e., the chances to avoid accidental collisions, increases under cooperation. Remarkably, these benefits yield no additional load to the mean society effort. Thus, the proposed strategy is socially compliant, and the humanoid agent can behave as "one of us.".

Item Type:Article
Uncontrolled Keywords:Navigation in dynamic situations; Cognition; Compact cognitive maps; Dynamic GPS; Decision making; Nonlinear dynamics
Subjects:Sciences > Mathematics
ID Code:31267
Deposited On:02 Jul 2015 11:42
Last Modified:12 Dec 2018 15:06

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