主动推理用于高维机械臂的交互介导控制
Active Inference for Interaction-Mediated Control of a High-Dimensional Robotic Arm
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- University of Glasgow（格拉斯哥大学）
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中文总结 AI 辅助
本文提出一种基于主动推理的交互介导控制架构，将用户交互视为潜在目标证据，通过交互代理器与控制器分工，在模拟多连杆机械臂上实现全臂目标覆盖，验证了高维控制的有效性。
中文摘要 AI 辅助
我们提出通过主动推理实现交互介导控制，作为人机交互（HCI）中高维控制问题的一种通用架构方法。该架构将用户交互重新定义为关于潜在任务目标的证据提供，而非对设备控制输入的直接指定。中介功能分布在交互代理器和主动推理控制器之间：交互代理器选择信息量大的用户查询，并对用户偏好执行贝叶斯推理；主动推理控制器则根据由此产生的偏好信息自主规划并执行动作以控制设备。这种分工将用户交互的语义与底层设备控制的语义解耦。我们在一个模拟的多连杆机械臂中实例化该架构，以执行同时的全臂目标覆盖任务。模拟用户仅通过离合器式二元评价通道进行交流，而不指定关节扭矩命令。随着机械臂维度的增加，该架构实现了成功的交互介导控制，尽管任务成功率低于控制器直接接收真实目标偏好时的成功率。精确的目标子集识别也仍不完美，这凸显了偏好推理与成功任务完成之间的区别。实验结果在受控、匹配模型假设下提供了对所提出架构的初步计算演示，并激励其在更广泛的人机交互应用中的进一步研究。
英文摘要
We propose interaction-mediated control via Active Inference as a general architectural approach to high-dimensional control problems in Human--Computer Interaction (HCI). This architecture recasts user interaction as the provision of evidence about a latent task objective, rather than the direct specification of plant-control inputs. The mediating function is distributed between an interaction broker, which selects informative user queries and performs Bayesian inference over user preferences, and an Active Inference controller, which autonomously plans and acts under the resulting preference information to control the plant. This division of labour decouples the semantics of user interaction from those of low-level plant control. We instantiate the architecture in a simulated, multi-link robotic arm to perform a simultaneous whole-arm target-coverage task. A simulated user communicates exclusively through a clutch-style binary evaluative channel, without specifying joint-torque commands. Across increasing arm dimensionalities, the architecture achieves successful interaction-mediated control, although task success is lower than when the controller receives the true target preferences directly. Exact target-subset identification also remains imperfect, highlighting the distinction between preference inference and successful task completion. The experimental findings provide an initial computational demonstration of the proposed architecture under controlled, matched-model assumptions and motivate its further investigation in broader HCI applications.
