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.

翻译：真实世界环境中的自主机器人导航需要探索以获取环境信息，同时需要目标导向的导航以到达指定目标。基于自由能原理的主动推理（AIF）通过最小化期望自由能（EFE），将认知价值与外在价值相结合，为这些行为提供了一个统一框架。为实现这一目标，我们提出了一种深度AIF框架，该框架集成了扩散策略作为策略模型，以及多时间尺度循环状态空间模型（MTRSSM）作为世界模型。扩散策略生成多样化的候选动作，而MTRSSM通过潜在想象预测这些动作的长期后果，从而选择能够最小化EFE的动作。真实世界导航实验表明，与基线方法相比，我们的框架实现了更高的成功率和更少的碰撞，尤其是在需要探索的场景中。这些结果突显了基于EFE最小化的AIF如何在真实世界机器人环境中统一探索与目标导向导航。