A4D creates functional latent spaces for affordance reasoning, reporting 94% accuracy on known affordances and over 90% on new ones with under 10% training data while enabling 100x faster inference.
Autogpt+ p: Affordance- based task planning with large language models
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
UniDomain extracts atomic PDDL domains from 12,393 robot videos to create a unified domain of 3137 operators and 2875 predicates, then retrieves and fuses relevant parts to enable zero-shot planning on unseen real-world tasks.
A survey of emerging AI agent architectures that organizes single and multi-agent designs around reasoning, planning, tool use, communication, and reflection phases.
citing papers explorer
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What Objects Enable, Not What They Are: Functional Latent Spaces for Affordance Reasoning
A4D creates functional latent spaces for affordance reasoning, reporting 94% accuracy on known affordances and over 90% on new ones with under 10% training data while enabling 100x faster inference.
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UniDomain: Pretraining a Unified PDDL Domain from Real-World Demonstrations for Generalizable Robot Task Planning
UniDomain extracts atomic PDDL domains from 12,393 robot videos to create a unified domain of 3137 operators and 2875 predicates, then retrieves and fuses relevant parts to enable zero-shot planning on unseen real-world tasks.
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The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey
A survey of emerging AI agent architectures that organizes single and multi-agent designs around reasoning, planning, tool use, communication, and reflection phases.