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Robust-WAM achieves superior out-of-distribution generalization in robot control by seamlessly integrating semantic foresight into action predictions while leveraging extensive VGM pretraining.
Achieving 98.0% success in cross-embodiment manipulation without manual action alignment could redefine how we approach robot control across diverse platforms.
Action-only decoding in GigaWorld-Policy-0.5 slashes inference latency to 85 ms, revolutionizing real-time robot control efficiency.
Ditch slow, multi-step video generation: S-VAM distills the structured generative priors of multi-step denoising into a single forward pass for real-time robot action prediction.
A practical VLA model, LLaVA-VLA, achieves strong generalization and versatility on a new benchmark, CEBench, while running on consumer-grade GPUs, eliminating the need for costly pre-training.
