RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in One-Shot

Understand

A key challenge in robotic manipulation in open domains is how to acquire diverse and generalizable skills for robots.

• Recent research in one-shot imitation learning has shown promise in transferring trained policies to new tasks based on demonstrations.
• This feature is attractive for enabling robots to acquire new skills and improving task and motion planning.
• However, due to limitations in the training dataset, the current focus of the community has mainly been on simple cases, such as push or pick-place tasks, relying solely on visual guidance.