embodied-intelligence

LingBot-Video, a 30B-parameter video model with sparse MoE, designed for embodied intelligence, is open-sourced. It outperforms existing models on RBench, trained on 70K+ hours of embodied data.

LingBot-Video presents a DiT-based video pretraining framework with Mixture-of-Experts architecture, specialized data augmentation, and multi-dimensional reward system for embodied intelligence applications.

A Peking University study published in Cell Reports finds that the human brain can adapt to virtual non-human limbs (wings) through VR training, showing dynamic plasticity in the occipitotemporal cortex.

Lin Junyang, former head of Alibaba's Qianwen team, closed his AI lab's first financing round at a $2B post-money valuation, with Gao Rong and Sequoia China each investing $100M and Tencent adding $20M. The lab will focus on world models and embodied intelligence rather than general LLMs.

This article delves into the concept of embodied intelligence, its intellectual origins (philosophy, cognitive science, AI robotics), and historical development (the failure of symbolism and Brooks' subsumption architecture). It analyzes its differences from pure software AI and the challenges it faces.

This paper proposes Embodied-BenchClaw, an autonomous multi-agent system that automatically constructs embodied spatial intelligence benchmarks from user intent through a five-stage pipeline with process quality control and an extensible Skill Library.

Shared an information gap report on the developer ecosystem, covering topics such as migrating openclaw-type projects to wearable devices like AI glasses and rings, open-source data and models for robotics and embodied AI, and niche open-source applications for AI API relay stations and routing.

GEM introduces a generative supervision method to improve embodied intelligence by leveraging generative models for training.

Qwen-VLA is a unified vision-language-action model for embodied decision-making, integrating manipulation, navigation, and trajectory prediction across different robot platforms. It uses a DiT-based action decoder and embodiment-aware prompt conditioning, achieving strong performance and out-of-distribution generalization.

Fei-Fei Li warns that AI is too focused on language models, emphasizing that the world is physical, visual, and spatial, and that most of the economy relies on embodied intelligence.