Good and bad news for blue-collar workers

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.

“H2O teleoperation is a framework based on reinforcement learning (RL) that facilitates the real-time whole-body teleoperation of humanoid robots using just an RGB camera,” Tairan He, CMU’s LeCAR (Learning and Control for Agile Robotics) Lab and one of the project’s lead researchers, told Tech Xplore. “The process starts by retargeting human motions to humanoid capabilities through a novel ‘sim-to-data’ methodology, ensuring the motions are feasible for the humanoid’s physical constraints. This refined motion dataset then trains an RL-based motion imitator in simulation, which is subsequently transferred to the real robot without further adjustment.”

Using this approach, the researchers could leverage images taken by an RGB camera – one that collects visible light and converts it to a color image replicating normal human vision – and a 3D pose estimator to enable the motions of human teleoperators to be mimicked by H2O.

The results, seen in videos taken by the researchers, speak for themselves. In them, H2O can be seen kicking a ball, binning a box, sidestepping while in a boxing stance, and walking (albeit like an unsteady toddler) with a stroller. To the researchers’ knowledge, this is the first demonstration of real-time whole-body humanoid teleoperation.

The CMU researchers described their approach in a paper posted on the arXiv pre-print website.

A longer video from the LeCAR Lab at CMU, below, shows off more of H2O’s capabilities, including punching an Amazon box while wearing boxing gloves and then performing a victory salute (I can’t help but wonder if that’s robot payback), jumping backwards, and being kicked in the back by a human to demonstrate its robustness.

Further research will examine the introduction of inputs other than from the human teleoperator, such as force feedback and verbal and conversational feedback, which could enhance H2O’s capabilities. And a promising direction for future research is incorporating lower-body tracking to enable the humanoid to perform more skillful human motions like sports and dancing.

This brings us back to the earlier question: Could H2O, or something like it, enable remote labor? CMU’s announcement regarding H2O has generated some thought-provoking discussion on Reddit’s r/Futurology subreddit.

Obviously, using teleoperated humanoids means humans get to keep their jobs (a positive). Another plus is employing these humanoids for risky work or search-and-rescue missions in remote and/or human-inaccessible regions.

However, there are some downsides, the most apparent one being related to labor. As pointed out by Reddit user lughnasadh, “[I]t opens up more avenues for job offshoring in rich countries.” Then there’s the slippery-slope argument: Teleoperated robots now, fully autonomous (read: job-stealing) robots later.

Interestingly, a 2022 study to gauge public acceptance of using teleoperated robots to perform labor jobs found that many people are open to remote work with lower pay than onsite roles. Again, it’s one of those ‘time will tell’ situations we’ve become accustomed to in the swiftly advancing AI/robotics space.

Source: CMU, Tech Xplore