arXiv AI
IMPACT: Attention Is the Interaction Map for Scalable Interaction-Aware World Model Training
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Acting in Meters: Learning Metric Interactions for Precise Robotic Manipulation
The paper introduces a metric interaction framework for robotic manipulation that explicitly models object- and scene-level interactions in Cartesian space. It uses Interaction‑Centric Tokens (ICTs) to represent end‑effector trajectories relative to objects and a Metric Action Interaction Field (MAIF) to attend to scene point‑cloud features for geometry‑conditioned action corrections. Experiments show modest but consistent improvements across several benchmarks, including LIBERO, RoboTwin 2.0, and real‑world tasks.
arXiv AI
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From Human Videos to Robot Manipulation: A Survey on Scalable Vision-Language-Action Learning with Human-Centric Data
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Hugging Face Trending Papers
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Acting in Meters: Learning Metric Interactions for Precise Robotic Manipulation
Vision-Language-Action models and World-Action Models have advanced language-conditioned robotic manipulation, yet often leave metric relations among actions, objects, and scene geometry implicit. Hum...
arXiv Machine Learning
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$\mu_0$: A Scalable 3D Interaction-Trace World Model
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arXiv AI
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Making Foresight Actionable: Repurposing Representation Alignment in World Action Models
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World Models for Embodied Intelligence: From Plausible to Controllable to Actionable
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