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.
> Our method currently solves the Rubik’s Cube 20% of the time when applying a maximally difficult scramble that requires 26 face rotations. For simpler scrambles that require 15 rotations to undo, the success rate is 60%.
And looking at the data in http://cube20.org/qtm/ , with a random cube, the probability to have a maximal-scrambled cube that needs 26 quarter-turns is 10^-20, but most (~75%) of the random cubes need 20 or 21 quarter-turns. Most of the algorithms don't use the most efficient path to solve the cube, so if the best path has 20 steps, the actual path will have a hundred or more steps.
To solve the cube in 15 steps, it must start as almost solved. It's not what people usually call "normal conditions".
Under "normal conditions", my reading is the success is 20%, 0% for a maximally-scrambled cube. I don't feel the Giiker Cube can't be considered "normal conditions".
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.