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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 6·5 min read
RESample: A Robust Data Augmentation Framework via Exploratory Sampling for Robotic Manipulation
TL;DR
RESample is a data augmentation framework designed to enhance robotic manipulation by addressing failure recovery. It improves policy success rates by up to 12% with minimal additional samples.
✦ Why It Matters
Implement RESample to enhance your robotic systems' ability to recover from execution failures today.
Key Takeaways
- Utilize RESample to improve robotic manipulation tasks.
- Incorporate failure recovery data into training datasets.
- Leverage exploratory sampling for better coverage of failure modes.
- Achieve significant performance gains with minimal additional data.
How It Works
RESample operates by first training a conservative coverage function that identifies failure cases within the existing data distribution. It then uses this information to perform exploratory sampling, generating new trajectories that include both exploratory behaviors and recovery actions.
This process effectively extends the training dataset to include scenarios that the robot may encounter in real-world applications, thereby improving its ability to recover from failures.