A Biologically Inspired Adaptive Working Memory System for Efficient Robot Control and Learning

NSF Grant EIA-0325641

Research Objectives and Approach

In the primate brain, the tension between the desire for flexibility and the need for efficiency is thought to be largely addressed by the interaction between working memory and executive control faculties in the prefrontal cortex (PFC) and systems supporting relatively automatic forms of behavior in more posterior areas. The efficient reactive processes of posterior brain areas typically drive behavior in common situations, and they are modulated by frontal systems when special circumstances arise. The frontal cortex is well equipped to maintain task-relevant information in a kind of working memory, and it is well positioned to guide action selection when flexibility is needed.

Inspired by the utility of the PFC in biological systems, we are implementing an adaptive working memory system for efficient robot control and learning based on computational neuroscience models, and we will assess the contribution of such a system to the successful performance of robot navigation and object manipulation tasks in dynamically changing environments.

Our objectives include:
  • To develop a software toolkit that encapsulates computational neuroscience models of the working memory circuits of the PFC, optimized for use in robot control systems.
  • To develop powerful perception algorithms capable of encoding sensory events as abstract and compact working memory chunks, highlighting salient features such as category membership of perceived objects and novelty of events.
  • To develop mechanisms for relating metric spatial representations to representations of a more qualitative and linguistic nature, allowing for the efficient recording of spatially distributed events and goals in working memory.
  • To develop cognitive architectures for flexible motor control, utilizing an adaptive working memory to guide the search for situation-appropriate motor programs to be integrated.
  • To demonstrate the utility of an adaptive working memory for robot control and, at the same time, provide evidence that current computational neuroscience accounts of PFC function will indeed scale to real-world tasks and situations.
Learning from experience is central to this effort. Perception algorithms being explored use a variety of machine learning methods. The proposed motor control architecture is grounded in adaptive models of the cerebellum and basal ganglia. The working memory system learns to identify informational chunks worthy of retention using a model of the interactions between the brain's dopamine (DA) system and the PFC. Together, these systems will learn both routine behaviors and flexible strategies for novel situations.

Figure 1: The Matilda Mobile Robot

Figure 2: Skeeter and Segway RMP

Broader Impacts

  • Computational methods facilitating the development of robots and other cognitive systems capable of learning from experience to respond flexibly in novel situations.
  • Refined computational neuroscience models of human memory, tested in the real world.
  • A freely disseminated open source software toolkit for robotic adaptive working memory systems – computational neuroscience results packaged for use by technologists.
  • Strengthened partnerships between researchers in the fields of robotics, machine learning, artificial intelligence, and cognitive neuroscience.

WM in Mobile Robotics

A mobile robot equipped with such a working memory system will not waste time processing most of the sensory information available to it as it navigates through space. Reactive systems will monitor the path ahead, implementing behaviors like obstacle avoidance using basic sensory cues. The working memory system will contain only information about the next few expected landmarks. The robot may retain information about the expected locations of such landmarks or about their expected sensory properties, and this information will be used to direct attention in perceptual processes. When a sought after landmark is finally discovered, the memory will have learned to free itself of the burden of retaining information about that landmark and to "gate into working memory" information about landmarks expected further down the path, retrieved from a long term store. Also, the location of a temporarily occluded landmark will be actively retained until, because of the robot's motion or because of motion of the occluding object, it becomes visible once again.

The Working Memory project homepage is maintained by the University of Missouri here.