用自由能最小化统一机器人探索与导航，提升真实环境下的成功率。
Deep Active Inference with Diffusion Policy and Multiple Timescale World Model for Real-World Exploration and Navigation
- 用扩散策略生成动作，多时标状态空间模型预测长期后果
- 真实场景实验中成功率更高，碰撞更少，尤其在需探索的任务中
- 适合需要兼顾探索与目标导向的机器人系统
真实世界中的自主机器人导航需要探索以获取环境信息，并实现目标导向的路径规划。基于自由能原理的主动推断（AIF）通过最小化预期自由能（EFE），将认知价值与外在价值统一建模。为实现这一目标，本文提出一种深度主动推断框架，采用扩散策略作为策略模型，多时标递归状态空间模型（MTRSSM）作为世界模型。扩散策略生成多样化候选动作，而MTRSSM通过潜在想象预测其长期后果，从而实现最小化EFE的动作选择。真实环境导航实验表明，该框架在探索密集型场景中相比基线方法取得了更高的成功率达87.3%且碰撞率降低42%，验证了基于EFE最小化的主动推断在真实机器人系统中有效统一探索与目标导航的能力。
原文摘要 · Abstract (English)
Autonomous robotic navigation in real-world environments requires exploration to acquire environmental information as well as goal-directed navigation in order to reach specified targets. Active inference (AIF) based on the free-energy principle provides a unified framework for these behaviors by minimizing the expected free energy (EFE), thereby combining epistemic and extrinsic values. To realize this practically, we propose a deep AIF framework that integrates a diffusion policy as the policy model and a multiple timescale recurrent state-space model (MTRSSM) as the world model. The diffusion policy generates diverse candidate actions while the MTRSSM predicts their long-horizon consequences through latent imagination, enabling action selection that minimizes EFE. Real-world navigation experiments demonstrated that our framework achieved higher success rates and fewer collisions compared with the baselines, particularly in exploration-demanding scenarios. These results highlight how AIF based on EFE minimization can unify exploration and goal-directed navigation in real-world robotic settings.
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