Training humanoid robots to do things seems to be the way to go compared to the “traditional” engineering approach. We can’t tell a robot how to play tennis but we can let it learn by trial and error.


As we have commented before, the age of the engineering approach to robotics is probably well and truly over. We no longer need to work out how to program the movements that are needed to get a job done – we can simply use reinforcement learning to get the robot to figure it out for itself. 

Of course, this isn’t as simple as it sounds and it takes a lot of computing power – but we are seeing more and more examples of it in action and working well. The latest is a humanoid robot that can play tennis – well it has learned how to return the ball and keep a rally going. While it has no strategy for playing tennis and isn’t trying to score points against its opponent, It is remarkable impressive and slightly spooky.  

The system is called LATENT Learns Athletic humanoid TEnnis skills from imperfect human motioN daTa – which is about as contrived an acronym as can be invented! However, don’t let this put you off as its an interesting and generalizable approach. The training data is imperfect motion capture – motion fragments that capture the primitive skills involved – forehand, backhand and footwork. It seems that it is possible to learn these fragments and put them together into a useful order and, with some additional reinforcement learning, the humanoid robot, an off-the-shelf Unitree G1, can master returning the ball using classic tennis strokes. This is all done in simulation and then, using a clever technique, is transferred to the real world robot.

As the research paper says:

“Our method achieves surprising results in the real world and can stably sustain multi-shot rallies with human players…”

Yes I have to agree and it seems such a short time ago we were laughing at humanoid robots attempting to walk like humans…

robottenis


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