通过交互智能重新定义机器人泛化能力
Redefining Robot Generalization Through Interactive Intelligence
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中文总结 AI 辅助
针对主流范式将机器人视为单一自主决策者、难以适配半自主人机交互场景的问题，本文提出受神经科学启发的四模块交互多智能体架构，强调机器人基础模型需转向该视角以提升泛化能力与性能。
中文摘要 AI 辅助
大规模机器学习的最新进展已产生高容量基础模型，能够适应广泛的下游任务。尽管此类模型在机器人领域具有巨大潜力，但主流范式仍将机器人描绘为单一的自主决策者，执行操作和导航等任务，人类参与有限。然而，包括可穿戴机器人（如假肢、矫形器、外骨骼）、远程操作和神经接口在内的大量现实世界机器人系统是半自主的，需要与人类伙伴持续互动协调，这对单智能体假设提出了挑战。在本立场文件中，我们认为机器人基础模型必须向交互多智能体视角演进，以应对实时人机协同适应的复杂性。我们提出一种受神经科学启发的可泛化架构，包含四个模块：（1）基于感觉运动整合原理的多模态传感模块；（2）让人联想到认知科学中联合行动框架的临时团队合作模型；（3）基于运动控制内部模型理论的预测性世界信念模型；（4）呼应赫布可塑性和强化可塑性概念的记忆/反馈机制。尽管通过可穿戴设备与人类生理密不可分的赛博格系统视角进行说明，但所提出的框架广泛适用于在半自主或交互环境中运行的机器人。通过超越单智能体设计，我们的立场强调了机器人基础模型如何实现更稳健、个性化和具有预见性的性能水平。
英文摘要
Recent advances in large-scale machine learning have produced high-capacity foundation models capable of adapting to a broad array of downstream tasks. While such models hold great promise for robotics, the prevailing paradigm still portrays robots as single, autonomous decision-makers, performing tasks like manipulation and navigation, with limited human involvement. However, a large class of real-world robotic systems, including wearable robotics (e.g., prostheses, orthoses, exoskeletons), teleoperation, and neural interfaces, are semiautonomous, and require ongoing interactive coordination with human partners, challenging single-agent assumptions. In this position paper, we argue that robot foundation models must evolve to an interactive multi-agent perspective in order to handle the complexities of real-time human-robot co-adaptation. We propose a generalizable, neuroscience-inspired architecture encompassing four modules: (1) a multimodal sensing module informed by sensorimotor integration principles, (2) an ad-hoc teamwork model reminiscent of joint-action frameworks in cognitive science, (3) a predictive world belief model grounded in internal model theories of motor control, and (4) a memory/feedback mechanism that echoes concepts of Hebbian and reinforcement-based plasticity. Although illustrated through the lens of cyborg systems, where wearable devices and human physiology are inseparably intertwined, the proposed framework is broadly applicable to robots operating in semi-autonomous or interactive contexts. By moving beyond single-agent designs, our position emphasizes how foundation models in robotics can achieve a more robust, personalized, and anticipatory level of performance.
