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SAM-RL: Sensing-aware model-based reinforcement learning via differentiable physics-based simulation and rendering
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DOI：10.1177/02783649241284653.png)
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Model-based reinforcement learning is recognized with the potential to be significantly more sample efficient than model-free reinforcement learning. How an accurate model can be developed automatically and efficiently from raw sensory inputs (such as images), especially for complex environments and tasks, is a challenging problem that hinders the broad application of model-based reinforcement learning in the real world. In this work, we propose a sensing-aware model-based reinforcement learning system called SAM-RL. Leveraging the differentiable physics-based simulation and rendering, SAM-RL automatically updates the model by comparing rendered images with real raw images and produces the policy efficiently. With the sensing-aware learning pipeline, SAM-RL allows a robot to select an informative viewpoint to monitor the task process. We apply our framework to real world experiments for accomplishing three manipulation tasks: robotic assembly, tool manipulation, and deformable object manipulation. We demonstrate the effectiveness of SAM-RL via extensive experiments. Videos are available on our project webpage.
Keyword:
Model-based reinforcement learning
differentiable physics simulation
differentiable rendering
robot manipulation
world model
simulation
real2sim2real
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5
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2.4K
被引数:
1.5W
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