RT Journal Article
T1 Prediction-for-CompAction: navigation in social environments using generalized cognitive maps
A1 Villacorta Atienza, José Antonio
A1 Calvo, Carlos
A1 Makarov Slizneva, Valeriy
AB 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.".
PB Springer Verlag
SN 0340-1200
YR 2015
FD 2015-06
LK https://hdl.handle.net/20.500.14352/24073
UL https://hdl.handle.net/20.500.14352/24073
LA eng
NO Spanish Ministry of Science and Innovation
NO Foundation INCE
DS Docta Complutense
RD 12 may 2025