Targeted Data Collection Achieves 96% Success in Contact-Rich Robotic Manipulation
Researchers introduce a method that focuses dense data collection on the critical, contact-rich segments of manipulation tasks, while using traditional planning for simpler free-space motions. By combining automated data collection with offline deep reinforcement learning, their approach achieves a 96% average success rate across four real-world tasks using only 2–2.5 hours of autonomous data, significantly outperforming the strongest baseline at 55%. The method also maintains high performance in out-of-distribution scenarios where end-to-end approaches typically struggle.
Why it matters: This work shows that targeted data collection can greatly improve the efficiency and generalization of robotic manipulation, reducing reliance on large datasets and teleoperation.
Full story at: arXiv Robotics ↗