We also have results with an uninstrumented cube (as described in section 7 in the paper, or "Behind the scenes: Rubik's Cube prototypes" in the blog post), which are slightly weaker (see Table 6 in the paper). The 20% number is an example of critics cherry-picking their facts — the success rate is 60% under normal conditions, but 20% with a maximally-scrambled cube (which would happen randomly with probability less than 10^-20).
Also note: success here means that the robot was able to perfectly unscramble the cube — which requires perfectly performing up to 100 moves — without dropping it once. What it means, in practical terms, is that you need to wait for a long time in order to witness even a single failure. If you pick up the cube and place it back in the hand, it'll get right back to solving.
Note that like with OpenAI Five, the success rate is more of a function of how far we've had time to push the system than something fundamental. We're not building commercial robots; we're in the business of making new AI breakthroughs. So the next step for our robotics team will be finding a new task that feels impossible today, and see what it takes to make it no longer feel impossible.