Learning from Human Demonstrations: A New Paradigm for Scalable Robot Data Acquisition

Location: Junior Stage

Acquiring diverse and large-scale real-world robot data remains a critical bottleneck in training generalizable robotic action models. Efficient and scalable data collection has thus emerged as a key research focus in robotics.
A widely used method is teleoperation, where humans either wear VR devices or operate a secondary robot to guide actions. While effective, these approaches are limited by hardware-specific constraints and require complex setups, hindering scalability.
An emerging alternative is to learn directly from human demonstrations without relying on teleoperation hardware. This paradigm allows robots to acquire task-relevant motion data by observing or interpreting natural human movements, offering a more flexible and hardware-agnostic solution.
In this talk, I will introduce a novel framework for robot data acquisition from human demonstrations. I will detail how it bypasses traditional teleoperation limitations and enables scalable learning across varied tasks and environments. By bridging the gap between human intent and robot execution, this method opens a promising direction for general-purpose robotic learning in the real world.