Carnegie Mellon University (CMU) researchers have developed H2O – Human2HumanOid – a reinforcement learning-based framework that allows a full-sized humanoid robot to be teleoperated by a human in real-time using only an RGB camera. Which begs the question: will manual labor soon be performed remotely?
A teleoperated humanoid robot allows for the performance of complex tasks that are – at least at this stage – too complex for a robot to perform independently. But achieving whole-body control of human-sized humanoids to replicate our movements in real-time is a challenging task. That’s where reinforcement learning (RL) comes in.
RL is a machine-learning technique that mimics humans’ trial-and-error approach to learning. Using the reward-and-punishment paradigm of RL, a robot will learn from the feedback of each action they perform and self-discover the best processing paths to achieve the desired outcome. Unlike machine learning, RL doesn’t require humans to label data pairs to direct the algorithm.
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