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Qwen-RobotManip achieves unprecedented generalization in robotic manipulation by effectively aligning diverse data sources, outperforming existing models across multiple challenging benchmarks.
PAIWorld resolves critical multi-view inconsistencies in robotic manipulation, outperforming existing models and unlocking new applications in the process.
Qwen-RobotNav achieves unprecedented flexibility in navigation tasks by allowing real-time reconfiguration of its observation strategy, setting new benchmarks in the field.
Language-driven video generation in Qwen-RobotWorld achieves unprecedented accuracy in predicting robotic actions, outperforming existing models across key benchmarks.
AllDayNav achieves nearly 100% success in lifelong navigation by using a self-evolving memory system that outperforms traditional mapping techniques.
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.
Photorealistic simulation with Gaussian Splatting and drivable avatars closes the reality gap, enabling embodied agents to learn human-aware navigation policies that generalize better to the real world.
