Search papers, labs, and topics across Lattice.
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OVIP-SG outperforms existing frameworks by preserving small object instances and enhancing retrieval accuracy, revolutionizing the mapping of fine-grained objects in 3D environments.
MVUCF achieves a remarkable 98.9% on LIBERO, showcasing a 22.4-point improvement on LIBERO-Plus and a 23.3-point increase in task success rates, all without additional inference costs.
Joint pretraining on Ego2Robot-synthesized data boosts robot generalization, achieving unprecedented scale and diversity in training datasets.
Explicitly incorporating contact priors into visuo-tactile policies can boost manipulation success rates by over 21% in real-world scenarios.
Qwen-RobotNav redefines navigation by allowing real-time reconfiguration of strategies, achieving unprecedented flexibility and performance across diverse tasks.
Qwen-RobotManip achieves a 20% relative improvement over the previous state-of-the-art in robotic manipulation, showcasing unprecedented generalization capabilities from diverse, open-source datasets.
Language-driven video generation in Qwen-RobotWorld achieves unprecedented accuracy in predicting robotic actions, outperforming existing models across key benchmarks.
One model to control them all: Qwen-VLA achieves impressive zero-shot generalization across diverse robotic tasks and embodiments by unifying vision-language-action modeling.
