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About this paper
Fereshteh Sadeghi, Alexander Toshev, Eric Jang, Sergey Levine
This paper presents a learning-based approach for viewpoint-invariant visual servoing using a deep recurrent neural network, enabling a robot to reach an object specified by a query image from varying camera viewpoints without explicit calibration. The method achieves robust control and effective sim-to-real transfer by training in a randomized simulation and applying targeted visual adaptation to real-world data.
Key insights
- A recurrent neural network can learn to implicitly self-calibrate the robot's actions in image space, handling significant viewpoint variations and uncalibrated settings.
- Temporal memory provided by recurrent units is crucial for discerning the effect of actions on visual feedback, outperforming reactive policies.
- Combining extensive domain randomization in simulation with targeted visual adaptation on limited real-world data effectively bridges the sim-to-real gap for visual control policies.
Summary by alphaXiv.
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