用耦合自回归代理实现多关节系统动态建模与控制
Coupled autoregressive active inference agents for control of multi-joint dynamical systems
- 多个子代理共享记忆，通过贝叶斯滤波推断参数
- 在双质量-弹簧-阻尼系统中成功学习动态并达成目标位置
- 耦合设计提升对系统不确定性的适应能力，适合复杂控制任务
我们提出一种主动推理代理，用于识别和控制由多个通过关节连接的物体构成的机械系统。该代理由多个标量自回归模型代理组成，通过共享记忆相互耦合。每个子代理通过贝叶斯滤波推断系统参数，并在有限时间范围内最小化预期自由能以实现控制。实验表明，这种耦合代理能够学习双质量-弹簧-阻尼系统的动力学特性，并通过探索与利用的平衡将系统驱动至目标位置。相比独立子代理，其在意外度（surprise）和目标对齐性方面表现更优。
原文摘要 · Abstract (English)
We propose an active inference agent to identify and control a mechanical system with multiple bodies connected by joints. This agent is constructed from multiple scalar autoregressive model-based agents, coupled together by virtue of sharing memories. Each subagent infers parameters through Bayesian filtering and controls by minimizing expected free energy over a finite time horizon. We demonstrate that a coupled agent of this kind is able to learn the dynamics of a double mass-spring-damper system, and drive it to a desired position through a balance of explorative and exploitative actions. It outperforms the uncoupled subagents in terms of surprise and goal alignment.
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