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

Abstract

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."

High-Dimensional Brain: A Tool for Encoding and Rapid Learning of Memories by Single Neurons

“…Then the dimension grows with the number of neurons in the clique. Multidimensional representation of spatiotemporal information in the brain is also implied in the concept of generalized cognitive maps (see, e.g., [47,7,46]). Within this theory, spatiotemporal relations between objects in the environment are encoded as static (cognitive) maps and represented as elements of an n-dimensional space (n 1).…”

Semantic Knowledge Representation for Strategic Interactions in Dynamic Situations

“…Time compaction is useful for navigation in different dynamic situations (Villacorta-Atienza et al, 2015). However, its power goes far beyond effective or "applied" cognition.…”

“…A GCM, in particular, contains images of motor-motifs in the form of points in some configuration space. Such an enormous dimension reduction (compaction of time) significantly reduces brain resources required for the planning of trajectories in complex situations, including motor interactions of humans (Villacorta-Atienza et al, 2015). It also enables building concepts out of motor-motifs by using the principle of the high-dimensional brain (Calvo et al, 2019;Gorban et al, 2019Gorban et al, , 2020Tyukin et al, 2019).…”

Waves in Isotropic Totalistic Cellular Automata: Application to Real-Time Robot Navigation

“…This has two drawbacks: i) the target must be a priori designated, which sometimes is unfeasible [21] and ii) single solution significantly reduces the agent flexibility. Meanwhile, an advanced cognitive robot capable of interacting with humans will demand diverse criteria to judge the optimality of solutions beyond the trajectory length [22,23].…”

“…Recently, we proposed to use the wave dynamics for building cognitive maps [24] (see also [25,26] for biophysical details). In this case, a wave front starting from the agent position virtually explores the environment and generates a so-called compact cognitive map [23]. Such an egocentric map contains information on possible collisions with obstacles in the Euclidean metric and can be used for planning multiple trajectories to multiple targets.…”