OLSF-TRS is a generalized sequential decision framework using structured combinatorial optimization and multi-agent reinforcement learning for order-tote-robot coordination in tote-handling robotic systems, with near-optimal performance on small scales and 8-30%+ improvements over heuristics onlarge
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The paper defines Agent AI as interactive multimodal systems that perceive grounded data and generate embodied actions, arguing this approach can mitigate hallucinations in foundation models.
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Omni-scale Learning-based Sequential Decision Framework for Order Fulfillment of Tote-handling Robotic Systems
OLSF-TRS is a generalized sequential decision framework using structured combinatorial optimization and multi-agent reinforcement learning for order-tote-robot coordination in tote-handling robotic systems, with near-optimal performance on small scales and 8-30%+ improvements over heuristics onlarge
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Agent AI: Surveying the Horizons of Multimodal Interaction
The paper defines Agent AI as interactive multimodal systems that perceive grounded data and generate embodied actions, arguing this approach can mitigate hallucinations in foundation models.