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τ₀-VLA is an open-source hierarchical robot foundation model and reference implementation for long-horizon manipulation. A memory-augmented high-level policy generates the next subtask and uses world-model-guided test-time computation to search over alternatives when additional reasoning is needed; a generalist low-level policy then executes the selected subtask across robot embodiments. The low-level policy combines a Qwen3.5 vision-language backbone with a Mixture-of-Transformers action expert trained through conditional flow matching, using a unified 40-dimensional state/action space and multimodal co-training on tens of thousands of hours of heterogeneous real-world robot data. The repository provides LeRobot-format data loading, prompting, masking and normalization, embodiment-specific adapters, training and post-training recipes, deployment contracts, a policy server, and open-loop evaluation tools. It includes an AgiBot World example subset and templates for other datasets and robots. Public v1 serving supports joint-control checkpoints; native end-effector data can be used for training but end-effector serving is not supported in that release. The reference environment uses Python 3.11, CUDA 12.8, and PyTorch 2.7.1. Code and model weights are released under the Apache License 2.0.

LingBot-Map is a feed-forward 3D foundation model from the Robbyant Team for streaming 3D reconstruction from image sequences and video. Its Geometric Context Transformer unifies coordinate grounding, dense geometric cues, and long-range drift correction through anchor context, a pose-reference window, and trajectory memory. The system uses paged KV-cache attention for streaming inference and supports interactive browser-based reconstruction with point-cloud visualization, keyframe intervals, windowed processing for long sequences, and sky masking. It also provides an offline rendering pipeline for image folders or video, along with evaluation pipelines for datasets including KITTI and Oxford Spires. The repository reports approximately 20 FPS inference at 518×378 resolution on sequences exceeding 10,000 frames, and documents PyTorch installation with FlashInfer as the recommended attention backend and native SDPA as a fallback.

LingBot-World 2.0, also called LingBot-World-Infinity, is an AI world-modeling system from the Robbyant Team for generating interactive video environments. Its causal pretraining paradigm is designed for an unbounded interaction horizon, while a distilled real-time variant is claimed to drive 720p video streams at 60 frames per second. The system supports diverse actions, including attacking, archery, spell-casting, and shooting, along with text-driven events. Its agentic harness uses a pilot agent to plan and execute character behavior and a director agent to synthesize new environmental elements as a scene progresses. The causal inference implementation processes video frames chunk by chunk with KV caching rather than processing the entire sequence at once. The release provides inference code and model weights, including a 14B causal-fast model, with a codebase built on Wan2.2; models are distributed through Hugging Face and ModelScope, and the real-time system can be tried through Reactor on the web or LingGuang on mobile. The project is licensed under CC BY-NC-SA 4.0 for non-commercial use. It states that deployment code will not be released and points to SGLang or flashdreams for self-deployment references.

An open-source set of reinforcement-learning training environments for Pollen Robotics' Microduck bipedal robot, built on mjlab with MuJoCo Warp and PPO. It trains walking, standing, recovery, sitting, ground-picking, kicking, rolling, and roller-skating policies, with flat, rough, slope, and backlash variants where applicable. The environments encode a sim-to-real pipeline using BAM actuator physics for Dynamixel XL330 servos, domain randomization for battery voltage, voltage sag, command delay, and friction, and simulated gear backlash with output-side encoder feedback. Policies use a shared 61-dimensional observation contract for runtime hot-swapping, are trained at 50 Hz, exported to ONNX with the observation normalizer embedded, and deployed by the Microduck runtime. Training runs locally through a CUDA GPU or can be submitted to Hugging Face Jobs; the project is licensed under Apache 2.0, while its 3D model files use CC BY-SA-NC.

TurboVLA is a compact vision-language-action model and official implementation for robotic manipulation, developed by researchers at Huazhong University of Science and Technology and Huawei Technologies. Instead of routing visual input through a large language model, it independently encodes visual observations and language instructions, exchanges information through lightweight bidirectional vision-language interaction, and uses a compact decoder to predict continuous action chunks. The repository includes training and evaluation code for LIBERO and RoboTwin, with model checkpoints released separately. The project reports 32 Hz inference on an RTX 4090 with less than 1 GB of VRAM; on LIBERO, it reports a 97.7% average success rate, 31.2 ms inference latency, and 0.2 billion parameters.