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INFIFORCE Intelligent Technology Co., Ltd., Hangzhou, China, The University of Hong Kong, Hong Kong SAR, China
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By explicitly modeling spatial interaction and manipulation intent, AIM achieves a 94% success rate on RoboTwin 2.0, suggesting that robot control can be drastically improved by explicitly reasoning about *where* to interact, not just *how* scenes evolve.
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.