Towards Conscious Service Robots
This work addresses the challenge of building more flexible and reliable service robots for real-world applications, but it is incremental as it builds on existing cognitive science concepts without presenting new empirical results.
The paper tackles the problem of non-stationarity and performance drops in real-world robotics by proposing to integrate human cognitive insights, such as causal models and metacognition, into service robots to enable adaptation to novel situations and self-monitoring.
Deep learning's success in perception, natural language processing, etc. inspires hopes for advancements in autonomous robotics. However, real-world robotics face challenges like variability, high-dimensional state spaces, non-linear dependencies, and partial observability. A key issue is non-stationarity of robots, environments, and tasks, leading to performance drops with out-of-distribution data. Unlike current machine learning models, humans adapt quickly to changes and new tasks due to a cognitive architecture that enables systematic generalization and meta-cognition. Human brain's System 1 handles routine tasks unconsciously, while System 2 manages complex tasks consciously, facilitating flexible problem-solving and self-monitoring. For robots to achieve human-like learning and reasoning, they need to integrate causal models, working memory, planning, and metacognitive processing. By incorporating human cognition insights, the next generation of service robots will handle novel situations and monitor themselves to avoid risks and mitigate errors.