Redefining Robot Generalization Through Interactive Intelligence
Sharmita Dey

TL;DR
This paper advocates for a shift from single-agent to multi-agent, interactive foundation models in robotics, emphasizing human-robot co-adaptation and proposing a neuroscience-inspired architecture for semi-autonomous systems.
Contribution
It introduces a novel, generalizable architecture for robot foundation models that incorporates multimodal sensing, teamwork, predictive modeling, and adaptive feedback inspired by neuroscience.
Findings
Proposes a four-module architecture for interactive robot models.
Highlights the importance of human-robot co-adaptation.
Broadly applicable to semi-autonomous and wearable robotic 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,…
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Taxonomy
TopicsComputability, Logic, AI Algorithms
