Communication between Lingodroids with different cognitive capabilities

Abstract

Previous studies have shown how Lingodroids, language learning mobile robots, learn terms for space and time, connecting their personal maps of the world to a publically shared language. One caveat of previous studies was that the robots shared the same cognitive architecture, identical in all respects from sensors to mapping systems. In this paper we investigate the question of how terms for space can be developed between robots that have fundamentally different sensors and spatial representations. In the real world, communication needs to occur between agents that have different embodiment and cognitive capabilities, including different sensors, different representations of the world, and different species (including humans). The novel aspects of these studies is that one robot uses a forward facing camera to estimate appearance and uses a biologically inspired continuous attractor network to generate a topological map; the other robot uses a laser scanner to estimate range and uses a probabilistic filter approach to generate an occupancy grid. The robots hold conversations in different locations to establish a shared language. Despite their different ways of sensing and mapping the world, the robots are able to create coherent lexicons for the space around them.

Evolving spatial and temporal lexicons across different cognitive architectures

“…Again, like the real iRat, Robot Operating System (ROS), a robot middle-ware solution, was used to provide the same interface to the robots' sensors in the simulator (Quigley et al, 2009). However, as in Heath et al (2013), although the two robots both have the base of an iRat, they also have fundamental sensory and cognitive differences. One robot (the iRat) is fitted with an omni-directional camera as the primary spatial sensor…”

“…The simulated environment (Figure 7.6d) is modeled on the Australia maze, a detailed, feature-rich, environment for the iRats that was used in Heath et al (2013) (see Figure 7.6c).…”

“…In more recent Lingodroids studies using the iRat robots, an overhead tracking camera was used to establish shared attention based on proximity (Heath et al, 2012a;Heath et al, 2013). The overhead camera provided robots with an entering and exiting signal designating co-location.…”