Deep Learning based Quasi-consciousness Training for Robot Intelligent Model

• URL: http://arxiv.org/abs/2501.18955v1
• Date: Fri, 31 Jan 2025 08:27:32 GMT
• Title: Deep Learning based Quasi-consciousness Training for Robot Intelligent Model
• Authors: Yuchun Li, Fang Zhang,
• Abstract summary: This paper explores a deep learning based robot intelligent model that renders robots learn and reason for complex tasks. Every robot must be subjected to at least 13 years of special school for training anthropomorphic behaviour patterns.
• Score: 0.6916967405051087
• License:
• Abstract: This paper explores a deep learning based robot intelligent model that renders robots learn and reason for complex tasks. First, by constructing a network of environmental factor matrix to stimulate the learning process of the robot intelligent model, the model parameters must be subjected to coarse & fine tuning to optimize the loss function for minimizing the loss score, meanwhile robot intelligent model can fuse all previously known concepts together to represent things never experienced before, which need robot intelligent model can be generalized extensively. Secondly, in order to progressively develop a robot intelligent model with primary consciousness, every robot must be subjected to at least 1~3 years of special school for training anthropomorphic behaviour patterns to understand and process complex environmental information and make rational decisions. This work explores and delivers the potential application of deep learning-based quasi-consciousness training in the field of robot intelligent model.

Related papers

• Redefining Robot Generalization Through Interactive Intelligence [0.0]
  We argue that robot foundation models must evolve to an interactive multi-agent perspective in order to handle the complexities of real-time human-robot co-adaptation. By moving beyond single-agent designs, our position emphasizes how foundation models in robotics can achieve a more robust, personalized, and anticipatory level of performance.
  arXiv  Detail & Related papers  (2025-02-09T17:13:27Z)
• Towards Conscious Service Robots [21.66931637743555]
  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.
  arXiv  Detail & Related papers  (2025-01-25T12:32:52Z)
• $π_0$: 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)
• RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation [68.70755196744533]
  RoboGen is a generative robotic agent that automatically learns diverse robotic skills at scale via generative simulation. Our work attempts to extract the extensive and versatile knowledge embedded in large-scale models and transfer them to the field of robotics.
  arXiv  Detail & Related papers  (2023-11-02T17:59:21Z)
• 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)
• 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)
• Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning [121.9708998627352]
  Recent work has shown that, in practical robot learning applications, the effects of adversarial training do not pose a fair trade-off. This work revisits the robustness-accuracy trade-off in robot learning by analyzing if recent advances in robust training methods and theory can make adversarial training suitable for real-world robot applications.
  arXiv  Detail & Related papers  (2022-04-15T08:12:15Z)
• From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence [113.06484656032978]
  Article argues that embodied intelligence is a key driver for the advancement of machine learning technology. We highlight challenges and opportunities specific to embodied intelligence. We propose research directions which may significantly advance the state-of-the-art in robot learning.
  arXiv  Detail & Related papers  (2021-10-28T16:04:01Z)
• A Model of Fast Concept Inference with Object-Factorized Cognitive Programs [3.4763296976688443]
  We present an algorithm that emulates the human cognitives of object factorization and sub-goaling, allowing human-level inference speed, improving accuracy, and making the output more explainable.
  arXiv  Detail & Related papers  (2020-02-10T18:48:40Z)

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.