arXiv Computer Vision
R2RI: A Multi-View Event and RGB Dataset for Robot-to-Robot Interaction
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arXiv AI
Jul 21
STAR: Skeletal Token Alignment and Rearrangement for Interaction Recognition
arXiv:2607. 17342v1 Announce Type: cross Abstract: Understanding physical human-robot and human-human interactions is a challenging yet emerging topic in 3D vision.
arXiv Computer Vision
1d ago
ECHO: Embodied Camera Observations of Human Object Carrying
arXiv:2610.10438v1 Announce Type: new Abstract: Embodied and assistive agents must do more than recognize objects: they must reason about where an object belongs given the layout of an environment an...
arXiv AI
Jul 2
EgoSim: Egocentric World Simulator for Embodied Interaction Generation
arXiv:2604. 01001v2 Announce Type: replace-cross Abstract: We introduce EgoSim, a closed-loop egocentric world simulator that generates spatially consistent interaction videos and persistently updates the underlying 3D scene state for continuous simulation.
Hugging Face Trending Papers
2d ago
ECHO: Embodied Camera Observations of Human Object Carrying
Embodied and assistive agents must do more than recognize objects: they must reason about where an object belongs given the layout of an environment and the habits of the people who live in it. Progre...
arXiv Computer Vision
Sep 7
MINT: A Unified Model for World-Space Camera and Hand Motion Estimation from Scalable Egocentric Pipeline Supervision
MINT is a foundation model that directly predicts world-space two-hand trajectories from egocentric RGB video, jointly estimating camera motion, hand states, and hand presence in a single spatiotemporal representation. It uses an open-source labeling pipeline, EGOPIPELINE, to generate large-scale pseudo-labels for pretraining, followed by fine-tuning on a small set of high-quality joint annotations. The model outperforms existing multi-stage approaches in accuracy and speed, and generalizes zero‑shot to unseen egocentric datasets.
arXiv Machine Learning
Sep 17
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.