AI News, Robot Uses Evil Alter Ego to Learn Reliable Grasping

Robot Uses Evil Alter Ego to Learn Reliable Grasping

Even with a big pile o’ robots, this takes a long time (thousands of robot-hours, at least), and while you can end up with a very nice generalized grasping framework at the end of it, that framework doesn’t have a very good idea of what a good grasp is.

Real-world grasping doesn’t work exactly like that, as most humans can attest to: Just because it’s possible to pick something up and not drop it does not necessarily mean that the way you’re picking it up is the best way, or even a particularly good way.

This is one of the nice things about robots: You can program them with adversarial alter egos such that they’re able to interfere with themselves, whether it’s causing one arm to shake, or using a second arm to more directly disturbthe first by attempting to snatch an object away.

The researchers demonstrated that theiradversarialstrategy can accelerate the training process and lead to a more robust system than an approach that doesn’t rely on an adversary.They also showed how it works, too, much better than simply trying lots of additional grasps without an adversary: After 3 iterations of training with shaking adversary, our grasp rate improves from 43 percent to 58 percent.

The [overall] result is a significant improvement over baseline in grasping of novel objects: an increase in overall grasp success rate to 82 percent(compared to 68 percentif no adversarial training is used).

Even more dramatically, if we handicapped the grasping by reducing maximum force and contact friction, the method achieved 65 percentsuccess rate (as compared to 47 percentif no adversarial training was used).

DexNet 2.0: 99% Precision Grasping

UC Berkeley AUTOLAB Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics. Jeffrey Mahler, Jacky Liang, Sherdil Niyaz,..