DREAM is a robotics project that incorporates sleep and dream-like processes within a cognitive architecture. This enables an individual robot or a group of robots to consolidate their experience into more useful and generic formats, thus improving their future ability to learn and adapt. DREAM relies on Evolutionary methods for discovery, optimization, re-structuring and consolidation of knowledge. This new paradigm will make the robot more autonomous in its acquisition, organization and use of knowledge and skills just as long as they comply with the satisfaction of pre-established basic motivations. DREAM will enable robots to cope with the complexity of being an information-processing entity in domains that are open-ended both in terms of space and time. It paves the way for a new generation of robots whose existence and purpose goes far beyond the mere execution of dull tasks.
Accumulating knowledge over long periods of time requires a consolidation process, so as to avoid being overwhelmed by the abundance of incoming information. Sleep has been shown to be critical for many consolidation processes, such as restructuring of representations, maintaining knowledge integration and coherence, improving insight learning, driving abstractions, forming novel levels of description, deleting unwanted information, exploring recombination of concepts, and stimulating creative thinking (Wagner et al., Nature, 2004). Our targeted scientific breakthrough is to enable robots to gain an open-ended understanding of the world over long periods of time, with alternating periods of experience and sleep. The possible benefits of sleep has so far been neglected in robotics and artificial intelligence.
To achieve higher levels of autonomy and understanding in developmental robotics, we propose a paradigm shift with DREAM, a cognitive architecture that exploits sleep to improve its functioning. It is contended here that Evolutionary methods (Fernando et al, Frontiers in Comp Neuro, 2012; Bellas et al., IEEE-TAMD, 2010) are a unifying principle for creative thinking and knowledge consolidation; these methods form the core of DREAM. Our key insight is that the brain consists of three coupled subsystems that are generated and adapted according to experience through evolutionary means: Models to make predictions about future state of the environment, notably to understand the results of actions; Policies that generate actions and behaviors, and are related to task-specific perceptual features; Values to reward, evaluate and compare policies or models. The long-term vision is to build genuinely situated and embodied agents with beliefs, desires, personalities, and idiosyncrasies, who are as inevitably influenced by their individual developmental trajectories as we are. To reach the proposed adaptive properties, the architecture will rely on alternating between active interaction and passive introspection over past events, i.e. sleep.
