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Official code for EWMBench: Evaluating Scene, Motion, and Semantic Quality in Embodied World Models
The main features of agibottech/ewmbench are: Embodied World Models, Robotics Benchmarks.
Projects with overlapping indexed features include: haosulab/maniskill — ManiSkill is a GPU-accelerated robot simulation framework designed for training robotic manipulation skills,… alibaba-damo-academy/worldvla — RynnVLA-002: A Unified Vision-Language-Action and World Model. anh-nn01/time-aware-world-model — ⏱️ Time-Aware World Model 🌎 🎓 Paper | 📌 Poster | 🌐 Website | 🎬 Videos. aria-zhangjl/storyweaver — This is an official implementation of AAAI 2025 paper StoryWeaver: A Unified World Model for Knowledge-Enhanced Story… chandar-lab/visa-for-mindjourney — ✨ Verification through Spatial Assertion (ViSA) ✨. aidanscannell/dcmpc — This repository is the official implementation of DC-MPC, presented in "Discrete Codebook World Models for Continuous…
ManiSkill is a GPU-accelerated robot simulation framework designed for training robotic manipulation skills, benchmarking learning algorithms, and generating synthetic datasets. It serves as a reinforcement learning environment where robot control policies can be developed and evaluated using parallelized physics and rendering on the GPU. The platform is distinguished by its ability to perform sim-to-real transfer, allowing policies trained in virtual environments to be deployed onto physical robotic hardware. It features ray-traced parallel rendering for producing high-frame-rate RGBD and se
RynnVLA-002: A Unified Vision-Language-Action and World Model
⏱️ Time-Aware World Model 🌎 🎓 Paper | 📌 Poster | 🌐 Website | 🎬 Videos
This repository is the official implementation of DC-MPC, presented in "Discrete Codebook World Models for Continuous Control" at ICLR 2025. DC-MPC is a model-based reinforcement learning algorithm demonstrating the strengths of learning a discrete latent space with discrete codebook encodings.