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Westlake University, Hangzhou, China
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VLA models can achieve higher precision and adaptability by leveraging 3D structural insights, leading to improved manipulation success rates.
DAGR transforms static goal representations into dynamic, state-aware embeddings, significantly boosting navigation performance in reinforcement learning tasks.
Agents using a structured memory framework can achieve significantly better manipulation performance, outperforming traditional methods in task completion and skill generalization.
Distributed ML slashes energy consumption in 6G IoT networks by up to 70% without sacrificing prediction accuracy, offering a greener path forward.
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.
