A big challenge when training AI models to control robots is gathering enough realistic data. Now, researchers at MIT have shown they can train a robot dog using 100 percent synthetic data.
Traditionally, robots have been hand-coded to perform particular tasks, but this approach results in brittle systems that struggle to cope with the uncertainty of the real world. Machine learning approaches that train robots on real-world examples promise to create more flexible machines, but gathering enough training data is a significant challenge.
One potential workaround is to train robots using computer simulations of the real world, which makes it far simpler to set up novel tasks or environments for them. But this approach is bedeviled by the “sim-to-real gap”—these virtual environments are still poor replicas of the real world and skills learned inside them often don’t translate.
Now, MIT CSAIL researchers have found a wayto combine simulations and generative AI to enable a robot, trained on …