AAAI 2026重磅！原力无限攻克具身智能“泛化”顽疾，定义因果AI新范式

Core Insights - The article emphasizes the importance of "generalization" in robotics, which is crucial for AI to transition from laboratory settings to real-world applications [1] - Traditional AI struggles with generalization due to its reliance on superficial correlations rather than understanding underlying causality [2] Industry Pain Points - The primary challenge in embodied intelligence is "out-of-distribution" (OOD) generalization, which hinders robots from adapting to new environments [4] - An example illustrates that if an AI learns to perform a task in a specific context (e.g., a red table), it may fail when the context changes (e.g., a blue table) due to false correlations [5][7] Key Breakthroughs - The introduction of causal inference as a core technology aims to enhance AI's logical reasoning capabilities, allowing robots to "see through phenomena to essence" [9] - The DSAP framework constructs a structured causal graph, differentiating between state-invariant variables (noise) and state-dependent variables (core causality) [10] - By implementing a disentangled structure-aware proxy, the algorithm mathematically "cuts off" environmental noise from decision-making, teaching robots to focus on core factors [13] Validation and Results - The research team validated the DSAP algorithm in complex tasks such as Alchemy and robotic manipulation, demonstrating its effectiveness in new environmental configurations [16][18] - Results showed that agents using the DSAP algorithm exhibited remarkable stability and significantly higher success rates compared to existing state-of-the-art algorithms in OOD tests [19][21] - The introduction of causal mechanisms has enabled robots to develop preliminary logical reasoning abilities, moving beyond mere pixel-level pattern matching [22] Collaborative Efforts - The paper represents a successful collaboration between industry and academia, showcasing the integration of theoretical innovation and practical validation [24] - The partnership with top universities has allowed the company to maintain a leading position in academic research while accelerating the validation cycle of cutting-edge algorithms [25]