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Towards Conscious Service Robots

Sven Behnke

TL;DR

The paper argues that real-world service robots must transcend end-to-end C0 learning to achieve human-like adaptability in open-ended environments. It advocates a conscious cognition-inspired architecture that integrates C1 global availability and C2 metacognition, enabling causal reasoning, planning, and self-monitoring, grounded in structured representations and inductive biases. By combining object-centered frames, relational inductive biases, world models, and foundation-model distillation, the approach aims for systematic generalization and improved data efficiency, validated first in photorealistic simulations before real-world deployment. If successful, this framework could dramatically enhance robustness, safety, and autonomy of service robots across domains while informing cognitive science about consciousness.

Abstract

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.

Towards Conscious Service Robots

TL;DR

The paper argues that real-world service robots must transcend end-to-end C0 learning to achieve human-like adaptability in open-ended environments. It advocates a conscious cognition-inspired architecture that integrates C1 global availability and C2 metacognition, enabling causal reasoning, planning, and self-monitoring, grounded in structured representations and inductive biases. By combining object-centered frames, relational inductive biases, world models, and foundation-model distillation, the approach aims for systematic generalization and improved data efficiency, validated first in photorealistic simulations before real-world deployment. If successful, this framework could dramatically enhance robustness, safety, and autonomy of service robots across domains while informing cognitive science about consciousness.

Abstract

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.
Paper Structure (8 sections, 6 figures)

This paper contains 8 sections, 6 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Challenges
  4. Human Cognitive Functions
  5. The Need for Conscious Robots
  6. Objectives
  7. Methodology
  8. Conclusions

Figures (6)

  • Figure 1: Human cognitive functions according to Kahneman kahneman2011thinking (System 1, System 2) and Dehaene et al. Dehaene:2017 (C0, C1, C2).
  • Figure 2: Scene perception and prediction on three levels: in the sensor coordinate frame (bottom), in 3D multimodal embeddings (center), and with objects and their relations (top).
  • Figure 3: Conscious planning. The WM state is rolled out using actions 1--4. Actions 1 and 3 are unfeasible (unreachable top grasp and unstable placement, respectively).
  • Figure 4: Predicting multiple plausible futures conditioned on latent variable $z$.
  • Figure 5: Modeling actions on multiple levels of abstraction.
  • ...and 1 more figures