robotwin-1b2fab3b·3 events·first seen Aliases: RoboTwin
Cortex is a new embodied agent framework that bridges the semantic gap between high-level Vision-Language Model (VLM) planning and low-level Vision-Language-Action (VLA) execution for long-horizon robotic manipulation. The system standardizes manipulation into 32 canonical skill primitives and uses an event-balanced sampling strategy to handle subtask transition ambiguity, enabling automatic annotation of over 4,000 hours of video data. Cortex outperforms monolithic baselines by 3.1% on Libero-long and 4.1% on RoboTwin benchmarks, and demonstrates zero-shot generalization to unseen real-world tasks such as multi-stage chemistry experiments.
AHA-WAM introduces a dual Diffusion Transformer architecture that decouples world prediction (low-frequency) from action execution (high-frequency) in robot manipulation policies, addressing the inefficiency of existing world-action models that force both branches to operate at the same temporal resolution. The system uses a rolling key-value memory video DiT as a long-horizon scene planner and a fast action DiT that queries layerwise latent context via joint attention, with Observation-Guided Video-Context Routing enabling asynchronous execution. On RoboTwin benchmarks, AHA-WAM achieves 92.80% average success and 78.3% on real-world tasks at 24.17 Hz, a 4.59x speedup over Fast-WAM, without robot-data pretraining.
Alibaba's Qwen team presents Qwen-VLA, a unified embodied foundation model that extends the Qwen vision-language stack to continuous action and trajectory generation via a DiT-based action decoder. The model is jointly pretrained on diverse data spanning manipulation trajectories, egocentric demonstrations, synthetic simulation, and navigation data, with embodiment-aware prompt conditioning to support multiple robot platforms. A unified action-and-trajectory prediction framework covers manipulation, navigation, and trajectory prediction tasks. Benchmarks show strong results: 97.9% on LIBERO, 73.7% on Simpler-WidowX, 69.0% OSR on R2R navigation, and 76.9% average OOD success in real-world ALOHA experiments.