PaLM-SayCan shows that a robot’s performance can be improved simply by enhancing the underlying language model. When the system was integrated with PaLM, compared to a less powerful baseline model, we saw a 14% improvement in the planning success rate, or the ability to map a viable approach to a task. We also saw a 13% improvement on the execution success rate, or ability to successfully carry out a task. This is half the number of planning mistakes made by the baseline method. The biggest improvement, at 26%, is in planning long horizon tasks, or those in which eight or more steps are involved. Here’s an example: “I left out a soda, an apple and water. Can you throw them away and then bring me a sponge to wipe the table?” Pretty demanding, if you ask me.

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