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ASAP
Overview :
ASAP (Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills) is an innovative two-stage framework aimed at addressing the dynamic mismatch between simulation and the real world, thereby enabling agile whole-body skills in humanoid robots. This technology significantly enhances a robot's adaptability and coordination in complex dynamic environments by pre-training movement tracking strategies and training a residual motion model with real-world data. Key advantages of ASAP include efficient use of data, substantial performance improvements, and precise control over complex movements, providing new directions for future humanoid robot development, especially in scenarios requiring high flexibility and adaptability.
Target Users :
This product is suitable for robotics researchers, AI developers, and enterprises or institutions looking to develop highly flexible humanoid robots. It helps them rapidly transfer and optimize complex movements, reducing development costs and time.
Use Cases
In the transfer from IsaacGym to IsaacSim, ASAP significantly improved the smoothness and accuracy of robot movements.
In the transfer from IsaacGym to Genesis, ASAP optimized the robot's dynamic performance through a residual motion model.
On the real-world Unitree G1 humanoid robot, ASAP achieved precise execution of complex movements such as lateral jumps and kicking.
Features
Achieve motion tracking strategies through simulation pre-training.
Train residual motion models with real-world data to compensate for dynamic discrepancies.
Align simulation with real-world physics to enhance skill transfer effectiveness.
Support various humanoid robot platforms, including both simulators and real robots.
Significantly reduce motion tracking errors while improving agility and coordination of actions.
How to Use
1. Utilize human motion data to pre-train movement tracking strategies in a simulated environment.
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2. Deploy the pre-trained strategies in the real world to collect actual trajectory data.
3. Train a residual motion model based on real data to compensate for dynamic discrepancies between simulation and reality.
4. Integrate the residual motion model into the simulator to fine-tune the pre-trained strategies.
5. Deploy the fine-tuned strategies in the real world to achieve agile whole-body skills.
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