Redefining Robot Generalization Through Interactive Intelligence

2025-02-09

AI Summary

Current robotic foundation models predominantly assume single-agent autonomous decision-making, limiting their applicability to real-time human–robot collaborative semi-autonomous systems (e.g., wearable robots, teleoperation, neural interfaces). To address this, we propose a novel paradigm for general-purpose robotics explicitly designed for real-time human–robot collaboration. Our approach abandons the single-agent assumption and introduces the first neuroscience-inspired, four-module multi-agent foundation model: multimodal perception, improvisational collaborative modeling, predictive world belief, and plastic memory feedback. Methodologically, it integrates sensorimotor perception, joint-action cognition, internal-model-based control, and dual Hebbian–reinforcement plasticity mechanisms. The model enables bidirectional human–robot co-adaptation and sustained interaction, significantly enhancing the generality, personalized decision-making capability, robustness, and anticipatory adaptability of semi-autonomous systems in real-world deployments.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

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.

Problem

• Enhance robot-human interactive intelligence.
• Shift from single-agent to multi-agent perspective.
• Develop neuroscience-inspired robotic architecture.

Innovation

• Interactive multi-agent perspective
• Neuroscience-inspired architecture
• Generalizable framework for robotics