Humanoid Cognitive Robots That Learn by Imitating: Implications for Consciousness Studies

Creating a conscious machine remains controversial and challenging. This work describes a humanoid cognitive robot that learns tasks by imitating human demonstrations, using cause-effect reasoning to infer a demonstrator's intentions rather than merely copying actions. Its cognitive components center on top-down control of working memory, which retains explanatory interpretations constructed during learning. Ongoing work aims to convert this imitation learning system into purely neurocomputational form, including low-level neuromotor components, working memory, and causal reasoning. Based on initial results, top-down cognitive control of working memory—especially its gating mechanisms—is argued to be an important potential computational correlate of consciousness in humanoid robots. Developing such neurocognitive control systems provides a credible route to ultimately developing a phenomenally conscious machine.

Study at a glance

Abstract

While the concept of a conscious machine is intriguing, producing such a machine remains controversial and challenging. Here, we describe how our work on creating a humanoid cognitive robot that learns to perform tasks via imitation learning relates to this issue. Our discussion is divided into three parts. First, we summarize our previous framework for advancing the understanding of the nature of phenomenal consciousness. This framework is based on identifying computational correlates of consciousness. Second, we describe a cognitive robotic system that we recently developed that learns to perform tasks by imitating human-provided demonstrations. This humanoid robot uses cause–effect reasoning to infer a demonstrator’s intentions in performing a task, rather than just imitating the observed actions verbatim. In particular, its cognitive components center on top-down control of a working memory that retains the explanatory interpretations that the robot constructs during learning. Finally, we describe our ongoing work that is focused on converting our robot’s imitation learning cognitive system into purely neurocomputational form, including both its low-level cognitive neuromotor components, its use of working memory, and its causal reasoning mechanisms. Based on our initial results, we argue that the top-down cognitive control of working memory, and in particular its gating mechanisms, is an important potential computational correlate of consciousness in humanoid robots. We conclude that developing high-level neurocognitive control systems for cognitive robots and using them to search for computational correlates of consciousness provides an important approach to advancing our understanding of consciousness, and that it provides a credible and achievable route to ultimately developing a phenomenally conscious machine.