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RHOS

πŸ›οΈ About RHOS Lab

RHOS studies Embodied AI, Physical Reasoning, and Human Activity Understanding. We are building a knowledge and reasoning-driven system that enables intelligent agents and robots to perceive human activities, reason about human behavior logics, learn skills from human activities, and interact with the environment.

Our homepage: https://mvig-rhos.com


πŸ”₯ Latest Release

OpenD4RT

An unofficial PyTorch/GPU implementation of D4RT for 4D reconstruction and tracking, with WorldTrack evaluation, visualization tools, and released Hugging Face checkpoints.

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πŸ“š Previous Works

Open4DHOI: Efficient and Scalable Monocular Human-Object Interaction Motion Reconstruction

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L1Flow: L1 Sample Flow for Efficient Visuomotor Learning

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IPR-1: Interactive Physical Reasoner

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RoboHiMan: A Hierarchical Evaluation Paradigm for Compositional Generalization in Long-Horizon Manipulation

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exUMI

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SIME: Enhancing Policy Self-Improvement with Modal-level Exploration

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Open3DHOI: Reconstructing In-the-Wild Open-Vocabulary Human-Object Interactions

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ImDy: Human Inverse Dynamics from Imitated Observations

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HDyS: Homogeneous Dynamics Space for Heterogeneous Humans

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ViFailback: Diagnose, Correct, and Learn from Manipulation Failures via Visual Symbols

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HAJL: Human-Robot Joint Learning for Efficient Robot Manipulation Skill Acquisition

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LLM-Inception: The Labyrinth of Links: Navigating the Associative Maze of Multi-modal LLMs

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Verb Mirage: Unveiling and Assessing Verb Concept Hallucinations in Multimodal Large Language Models

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HAKE-AVA

Fine-grained spatio-temporal activity understanding based on AVA videos, as part of the HAKE project.

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