用主动推理实现机器人长程重排任务，无需离线训练即可自适应调整。
Mobile Manipulation with Active Inference for Long-Horizon Rearrangement Tasks
- 分层主动推理架构，高阶选技能，低阶控制全身动作
- 在Habitat基准上三项长程任务均超越现有最优方法
- 支持在线调整与失败恢复，适合复杂现实场景应用
尽管主动推理在机器人控制中日益受到关注，但其在复杂、长程任务中的应用仍未经验证。本文提出一种全分层的主动推理架构，用于真实机器人环境中的目标导向行为。模型包含高层主动推理模块，用于在离散技能间进行选择，这些技能由全身主动推理控制器实现。该统一方法实现了灵活的技能组合、在线适应能力以及任务失败后的恢复能力，且无需离线训练。在移动操作的Habitat基准上，该方法在三个长程任务中均优于当前最先进的基线，首次证明主动推理可扩展至现代机器人基准的复杂程度。
原文摘要 · Abstract (English)
Despite growing interest in active inference for robotic control, its application to complex, long-horizon tasks remains untested. We address this gap by introducing a fully hierarchical active inference architecture for goal-directed behavior in realistic robotic settings. Our model combines a high-level active inference model that selects among discrete skills realized via a whole-body active inference controller. This unified approach enables flexible skill composition, online adaptability, and recovery from task failures without requiring offline training. Evaluated on the Habitat Benchmark for mobile manipulation, our method outperforms state-of-the-art baselines across the three long-horizon tasks, demonstrating for the first time that active inference can scale to the complexity of modern robotics benchmarks.
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