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😼 优雅地使用基于 clash/mihomo 的代理环境
Building General-Purpose Robots Based on Embodied Foundation Model
[ICLR 2025 Oral] The official implementation of "Diffusion-Based Planning for Autonomous Driving with Flexible Guidance"
[ICLR 2026] The official implementation of "Dichotomous Diffusion Policy Optimization"
StarVLA: A Lego-like Codebase for Vision-Language-Action Model Developing
[IEEE TGRS] The Official Implementation of "UAGLNet: Uncertainty-Aggregated Global-Local Fusion Network with Cooperative CNN-Transformer for Building Extraction"
The official implementation for [NeurIPS2025 Oral] Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free
Official PyTorch Implementation of "Diffusion Transformers with Representation Autoencoders"
[ICLR 2026] The offical Implementation of "Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model"
😼 优雅地使用基于 clash/mihomo 的代理环境
[ACM CSUR 2025] Understanding World or Predicting Future? A Comprehensive Survey of World Models
A benchmark for offline goal-conditioned RL and offline RL
A collection of robotics simulation environments for reinforcement learning
[ICLR 2026] SimpleVLA-RL: Scaling VLA Training via Reinforcement Learning
The official implementation of "Robo-MUTUAL: Robotic Multimodal Task Specification via Unimodal Learning"
[ICML 2025] The Official Implementation of "Efficient Robotic Policy Learning via Latent Space Backward Planning"
[NeurIPS 2025] SpatialLM: Training Large Language Models for Structured Indoor Modeling
🤗 LeRobot: Making AI for Robotics more accessible with end-to-end learning
[Lumina Embodied AI] 具身智能技术指南 Embodied-AI-Guide
[CVPR 2025] The offical Implementation of "Universal Actions for Enhanced Embodied Foundation Models"
Benchmarking Knowledge Transfer in Lifelong Robot Learning
[ICML 2024] The offical Implementation of "DecisionNCE: Embodied Multimodal Representations via Implicit Preference Learning"
A collection of papers on the topic of ``Computer Vision in the Wild (CVinW)''
[CVPR'23] Code for "SCOTCH and SODA: A Transformer Video Shadow Detection Framework".
Samples for CUDA Developers which demonstrates features in CUDA Toolkit