贝叶斯推断与具身智能的联系探索：迈向开放物理世界的具身AI系统

Core Insights and Background - The article explores the deep conceptual connection between Bayesian statistics and embodied intelligence, emphasizing that cognitive abilities fundamentally arise from real-time sensor interactions between agents and their environments [3] - Bayesian statistics provides a principled probabilistic framework for continuously reasoning under uncertainty by representing knowledge as probability distributions and updating beliefs based on new evidence [3] - Despite this connection, Bayesian principles are not widely applied in current embodied intelligence systems, which are analyzed through the lenses of search and learning, as highlighted by Rich Sutton in "The Bitter Lesson" [3][4] Search and Learning: Foundations of Modern AI - Search and learning are identified as universal methods driving significant breakthroughs in AI as computational power increases, with search involving systematic exploration of potential solutions and learning focusing on training models through data [4] - Sutton's insight indicates that while researcher-designed systems may succeed initially, they often hit performance bottlenecks, whereas systems built on scalable general methods like search and learning continue to improve with increased computational resources [4] Current Practices in Embodied Intelligence - Mainstream embodied intelligence methods are based on advancements in AI foundational models, such as pre-trained large language models and vision-language models, which provide rich prior knowledge about the world for embodied agents like robots [5] - However, these foundational models are insufficient for all requirements of embodied intelligence systems, as the encoded prior knowledge is static and coarse, lacking the precision needed for dynamic environments [6] Approaches to Addressing Limitations - Two primary approaches are identified to address the limitations of foundational models: embedding search operations within model training or fine-tuning processes in data-driven learning paradigms, and incorporating explicit search mechanisms for planning, similar to those used in AlphaGo and AlphaZero [7] Deep Connection Between Bayesian and Embodied Intelligence - From a philosophical perspective, Bayesianism and embodied intelligence are closely linked, with Bayesianism quantifying subjective beliefs and emphasizing dynamic knowledge updates through evidence [8] - Both frameworks share a common learning mechanism that views cognition/intelligence as a process dependent on dynamic interactions rather than static data, aligning with the paradigm of emergent intelligence [8] Gaps Between Bayesian Methods and Current Practices - There is a significant gap between Bayesian methods and current practices in embodied intelligence, particularly in learning and search, as Bayesian learning methods often rely on structured priors or explicit model assumptions that may hinder scalability [9] - A comparison highlights fundamental differences in model dependency, human knowledge injection frequency, learning scalability, and search methods between Bayesian intelligence and Sutton's preferred approaches [9] Future of Embodied Intelligence Shaped by Bayesian Methods - Modern embodied intelligence systems, especially those based on deep learning and large pre-trained models, have adopted data-driven, hypothesis-light methods that align with Sutton's preferences [10] - These systems can be constructed using pre-trained foundational models as building blocks, supplemented with additional modules for memory, atomic skill models, perception, sensor control, and navigation [11] Strategies for Data Scarcity - In scenarios of data scarcity, two mitigation strategies are proposed: collecting human demonstration data and resorting to simulations to create digital counterparts of the physical world [12] - Current large pre-trained models are seen as rough approximations of world models, insufficient for supporting embodied intelligence in rich, dynamic, and three-dimensional physical environments [12] Goals for Open Physical Environments - The ultimate goal for embodied intelligence is to operate in open physical environments, where knowledge and skills acquired in closed settings serve as prior knowledge [12] - In open worlds, embodied agents must continuously adapt their behavior through real-time sensor interactions, necessitating ongoing reasoning under uncertainty [12] Bayesian Methods for Complex Systems - Various existing Bayesian methods have been developed for global optimization in complex systems, particularly where traditional gradient-based methods are unsuitable [13] - The flexibility and generalization capabilities in real-world scenarios can be enhanced by relaxing the dependency on structured model assumptions, allowing for operations on model collections rather than committing to a single fixed model [13]