Robots Learn Faster With New Exoskeleton Data Method
Scientists worldwide are racing to advance humanoid robotics, with major projects like NVIDIA's GR00T and Google's Gemini Robotics leading the charge. A breakthrough technique using exoskeleton data is cutting costs and speeding up learning processes.
Researchers have discovered that robots can acquire new skills simply by analysing data from wearable exoskeletons. This method bypasses the need for expensive, time-consuming data collection, making robot learning more efficient.
In a recent study, a robot maintained balance and recovered from physical disturbances using this technique. Remarkably, training with exoskeleton data boosted its success in a pick-and-place task from a mere 5% to an impressive 80%.
LiDAR sensors, AI models, and other advanced technologies convert exoskeleton motion into robot-ready actions. This allows humanoid robots, which traditionally struggle to generalize human motion due to limited data, to learn and adapt more effectively.
In a separate development, a Unitree G1 humanoid robot learned to walk after just five teleoperated demonstrations. This rapid learning is a significant step forward in the field of humanoid robotics.
The HumanoidExo project, using a wearable suit to record full-body motion, and the HumanoidExo-VLA dual-layer AI system, are at the forefront of this innovative approach. By mimicking human motion captured by the exoskeleton, robots can learn to walk and perform other complex tasks more quickly and cost-effectively. As global research continues, these advancements promise a future where humanoid robots can learn and adapt more like humans.
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