A scalable reinforcement learning–based framework to facilitate the teleoperation of humanoid robots
The effective operation of robots from a distance, also known as teleoperation, could allow humans to complete a vast range of manual tasks remotely, including risky and complex procedures. Yet teleoperation could also be used to compile datasets of human motions, which could help to train humanoid robots on new tasks.
Teleoperation for robot imitation learning is bottlenecked by hardware availability. Can high-quality robot data be collected without a physical robot? We present a system for augmenting Apple Vision Pro with real-time virtual robot feedback. By providing users with an intuitive understanding of how their actions translate to robot motions, we…
Humanoid robots, which have a body structure that mirrors that of humans, could rapidly and effectively tackle a wide range of tasks in real-world settings. These robots and their underlying control algorithms have improved considerably in recent years. Many of them can now move faster, emulating various human-like movements.