From Understanding the World to Intervening in It: A Unified Multi-Scale Framework for Embodied Cognition

• URL: http://arxiv.org/abs/2503.00727v1
• Date: Sun, 02 Mar 2025 04:43:08 GMT
• Title: From Understanding the World to Intervening in It: A Unified Multi-Scale Framework for Embodied Cognition
• Authors: Maijunxian Wang,
• Abstract summary: We propose AUKAI, an Adaptive Unified Knowledge-Action Intelligence for embodied cognition that seamlessly integrates perception, memory, and decision-making via multi-scale error feedback.<n>We present a hybrid implementation that combines the strengths of neural networks with symbolic reasoning modules, thereby enhancing interpretability and robustness.<n>We demonstrate the potential of AUKAI through a detailed application in robotic navigation and obstacle avoidance, and we outline comprehensive experimental plans to validate its effectiveness in both simulated and real-world environments.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose AUKAI, an Adaptive Unified Knowledge-Action Intelligence for embodied cognition that seamlessly integrates perception, memory, and decision-making via multi-scale error feedback. Interpreting AUKAI as an embedded world model, our approach simultaneously predicts state transitions and evaluates intervention utility. The framework is underpinned by rigorous theoretical analysis drawn from convergence theory, optimal control, and Bayesian inference, which collectively establish conditions for convergence, stability, and near-optimal performance. Furthermore, we present a hybrid implementation that combines the strengths of neural networks with symbolic reasoning modules, thereby enhancing interpretability and robustness. Finally, we demonstrate the potential of AUKAI through a detailed application in robotic navigation and obstacle avoidance, and we outline comprehensive experimental plans to validate its effectiveness in both simulated and real-world environments.

Related papers

• Deep Learning for Resilient Adversarial Decision Fusion in Byzantine Networks [0.43512163406551996]
  This paper introduces a deep learning-based framework for resilient decision fusion in adversarial multi-sensor networks. The proposed approach employs a deep neural network trained on a globally constructed dataset to generalize across all cases without requiring adaptation.
  arXiv  Detail & Related papers  (2024-12-17T10:02:04Z)
• Unifying Perspectives: Plausible Counterfactual Explanations on Global, Group-wise, and Local Levels [2.675793767640172]
  Counterfactual Explanations (CFs) have emerged as a promising technique within Explainable AI (xAI) We introduce a novel unified approach for generating Local, Group-wise, and Global Counterfactual Explanations for differentiable classification models. Our work significantly advances the interpretability and accountability of AI models, marking a step forward in the pursuit of transparent AI.
  arXiv  Detail & Related papers  (2024-05-27T20:32:09Z)
• Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation [50.01551945190676]
  Social robot navigation can be helpful in various contexts of daily life but requires safe human-robot interactions and efficient trajectory planning. We propose a systematic relational reasoning approach with explicit inference of the underlying dynamically evolving relational structures. We demonstrate its effectiveness for multi-agent trajectory prediction and social robot navigation.
  arXiv  Detail & Related papers  (2024-01-22T18:58:22Z)
• Constrained Bayesian Optimization with Adaptive Active Learning of Unknown Constraints [10.705151736050967]
  optimizing objectives under constraints is a common scenario in real-world applications such as scientific experimental design, design of medical therapies, and industrial process optimization. We propose an efficient CBO framework that intersects the ROIs identified from each aspect to determine the general ROI. We showcase the efficiency and robustness of our proposed CBO framework through empirical evidence and discuss the fundamental challenge of deriving practical regret bounds for CBO algorithms.
  arXiv  Detail & Related papers  (2023-10-12T22:32:00Z)
• Generative Intrinsic Optimization: Intrinsic Control with Model Learning [5.439020425819001]
  Future sequence represents the outcome after executing the action into the environment. Explicit outcomes may vary across state, return, or trajectory serving different purposes such as credit assignment or imitation learning. We propose a policy scheme that seamlessly incorporates the mutual information, ensuring convergence to the optimal policy.
  arXiv  Detail & Related papers  (2023-10-12T07:50:37Z)
• Probabilistic Constrained Reinforcement Learning with Formal Interpretability [2.990411348977783]
  We propose a novel Adaptive Wasserstein Variational Optimization, namely AWaVO, to tackle these interpretability challenges. Our approach uses formal methods to achieve the interpretability for convergence guarantee, training transparency, and intrinsic decision-interpretation. In comparison with state-of-theart benchmarks including TRPO-IPO, PCPO and CRPO, we empirically verify that AWaVO offers a reasonable trade-off between high performance and sufficient interpretability.
  arXiv  Detail & Related papers  (2023-07-13T22:52:22Z)
• Networked Communication for Decentralised Agents in Mean-Field Games [59.01527054553122]
  We introduce networked communication to the mean-field game framework. We prove that our architecture has sample guarantees bounded between those of the centralised- and independent-learning cases. We show that our networked approach has significant advantages over both alternatives in terms of robustness to update failures and to changes in population size.
  arXiv  Detail & Related papers  (2023-06-05T10:45:39Z)
• Modeling Multiple Views via Implicitly Preserving Global Consistency and Local Complementarity [61.05259660910437]
  We propose a global consistency and complementarity network (CoCoNet) to learn representations from multiple views. On the global stage, we reckon that the crucial knowledge is implicitly shared among views, and enhancing the encoder to capture such knowledge can improve the discriminability of the learned representations. Lastly on the local stage, we propose a complementarity-factor, which joints cross-view discriminative knowledge, and it guides the encoders to learn not only view-wise discriminability but also cross-view complementary information.
  arXiv  Detail & Related papers  (2022-09-16T09:24:00Z)
• Trusted Multi-View Classification with Dynamic Evidential Fusion [73.35990456162745]
  We propose a novel multi-view classification algorithm, termed trusted multi-view classification (TMC) TMC provides a new paradigm for multi-view learning by dynamically integrating different views at an evidence level. Both theoretical and experimental results validate the effectiveness of the proposed model in accuracy, robustness and trustworthiness.
  arXiv  Detail & Related papers  (2022-04-25T03:48:49Z)
• Active Inference in Robotics and Artificial Agents: Survey and Challenges [51.29077770446286]
  We review the state-of-the-art theory and implementations of active inference for state-estimation, control, planning and learning. We showcase relevant experiments that illustrate its potential in terms of adaptation, generalization and robustness.
  arXiv  Detail & Related papers  (2021-12-03T12:10:26Z)
• Efficient Empowerment Estimation for Unsupervised Stabilization [75.32013242448151]
  empowerment principle enables unsupervised stabilization of dynamical systems at upright positions. We propose an alternative solution based on a trainable representation of a dynamical system as a Gaussian channel. We show that our method has a lower sample complexity, is more stable in training, possesses the essential properties of the empowerment function, and allows estimation of empowerment from images.
  arXiv  Detail & Related papers  (2020-07-14T21:10:16Z)

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.