Pretrained robot policies need post-training to become deployable
Robots can now perform complex behaviors, but their pretrained policies are still too unreliable for homes or factories: a system that loads dishes correctly 95% of the time can still break something every week. The proposed fix is a shared post-training recipe borrowed from language models: define environments and rewards, optimize against a reference model, then check for failures such as reward hacking. That would turn impressive demonstrations into systems people can use.