Towards Conscious Service Robots

• URL: http://arxiv.org/abs/2501.15198v1
• Date: Sat, 25 Jan 2025 12:32:52 GMT
• Title: Towards Conscious Service Robots
• Authors: Sven Behnke,
• Abstract summary: Real-world robotics face challenges like variability, high-dimensional state spaces, non-linear dependencies, and partial observability. 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. Next generation of service robots will handle novel situations and monitor themselves to avoid risks and mitigate errors.
• Score: 21.66931637743555
• License:
• 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.

Related papers

• $π_0$: A Vision-Language-Action Flow Model for General Robot Control [77.32743739202543]
  We propose a novel flow matching architecture built on top of a pre-trained vision-language model (VLM) to inherit Internet-scale semantic knowledge. We evaluate our model in terms of its ability to perform tasks in zero shot after pre-training, follow language instructions from people, and its ability to acquire new skills via fine-tuning.
  arXiv  Detail & Related papers  (2024-10-31T17:22:30Z)
• Commonsense Reasoning for Legged Robot Adaptation with Vision-Language Models [81.55156507635286]
  Legged robots are physically capable of navigating a diverse variety of environments and overcoming a wide range of obstructions. Current learning methods often struggle with generalization to the long tail of unexpected situations without heavy human supervision. We propose a system, VLM-Predictive Control (VLM-PC), combining two key components that we find to be crucial for eliciting on-the-fly, adaptive behavior selection.
  arXiv  Detail & Related papers  (2024-07-02T21:00:30Z)
• Growing from Exploration: A self-exploring framework for robots based on foundation models [13.250831101705694]
  We propose a framework named GExp, which enables robots to explore and learn autonomously without human intervention. Inspired by the way that infants interact with the world, GExp encourages robots to understand and explore the environment with a series of self-generated tasks.
  arXiv  Detail & Related papers  (2024-01-24T14:04:08Z)
• Exploring the effects of robotic design on learning and neural control [0.0]
  dissertation focuses on the development of robotic bodies, rather than neural controllers. I have discovered that robots can be designed such that they overcome many of the current pitfalls encountered by neural controllers in multitask settings.
  arXiv  Detail & Related papers  (2023-06-06T15:17:34Z)
• Incremental procedural and sensorimotor learning in cognitive humanoid robots [52.77024349608834]
  This work presents a cognitive agent that can learn procedures incrementally. We show the cognitive functions required in each substage and how adding new functions helps address tasks previously unsolved by the agent. Results show that this approach is capable of solving complex tasks incrementally.
  arXiv  Detail & Related papers  (2023-04-30T22:51:31Z)
• Self-Improving Robots: End-to-End Autonomous Visuomotor Reinforcement Learning [54.636562516974884]
  In imitation and reinforcement learning, the cost of human supervision limits the amount of data that robots can be trained on. In this work, we propose MEDAL++, a novel design for self-improving robotic systems. The robot autonomously practices the task by learning to both do and undo the task, simultaneously inferring the reward function from the demonstrations.
  arXiv  Detail & Related papers  (2023-03-02T18:51:38Z)
• World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges [51.92834011423463]
  We focus on the two concepts of world models and predictive coding. In neuroscience, predictive coding proposes that the brain continuously predicts its inputs and adapts to model its own dynamics and control behavior in its environment.
  arXiv  Detail & Related papers  (2023-01-14T06:38:14Z)
• Neuroscience-inspired perception-action in robotics: applying active inference for state estimation, control and self-perception [2.1067139116005595]
  We discuss how neuroscience findings open up opportunities to improve current estimation and control algorithms in robotics. This paper summarizes some experiments and lessons learned from developing such a computational model on real embodied platforms.
  arXiv  Detail & Related papers  (2021-05-10T10:59:38Z)
• Cognitive architecture aided by working-memory for self-supervised multi-modal humans recognition [54.749127627191655]
  The ability to recognize human partners is an important social skill to build personalized and long-term human-robot interactions. Deep learning networks have achieved state-of-the-art results and demonstrated to be suitable tools to address such a task. One solution is to make robots learn from their first-hand sensory data with self-supervision.
  arXiv  Detail & Related papers  (2021-03-16T13:50:24Z)
• A Survey of Behavior Learning Applications in Robotics -- State of the Art and Perspectives [44.45953630612019]
  Recent success of machine learning in many domains has been overwhelming. We will give a broad overview of behaviors that have been learned and used on real robots.
  arXiv  Detail & Related papers  (2019-06-05T07:54:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.