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Imagine automating the tedious engineering tasks in embodied AI development with a conversational agent, freeing researchers to focus on core algorithmic innovation.
Forget painstakingly collecting real-world defect data: high-fidelity synthetic anomalies, automatically generated from product designs using an MLLM, can dramatically improve 3D anomaly detection.
Stop struggling with compounding errors in long-horizon robotic tasks: AtomVLA leverages LLMs and latent world models to decompose tasks and score actions, boosting success rates to 97% on LIBERO.
