Enable robots to execute complex natural language instructions
SayCan helps robots understand and act on high-level commands given in everyday language, like cleaning up a spill or fetching a drink. It combines the smarts of large language models with real-world robot skills to ensure actions are actually possible in the environment. This means robots can handle multi-step tasks without needing humans to break everything down step by step. It's a big step toward making robots more helpful in homes and workplaces.
Connects language models to real-world robot capabilities
Chooses feasible actions using probability scores for success
Handles multi-step instructions for complex activities
Works with instructions in languages like Chinese and Spanish
Uses reasoning prompts to solve tricky tasks
It uses language models to break down instructions into feasible skills and combines them with value functions to ensure actions are contextually appropriate.
It achieves an 84% plan success rate and 74% execution success rate on tested tasks, reducing errors by half compared to previous models.
Yes, it handles multilingual queries like Chinese, French, and Spanish with almost no drop in performance, as the language model was trained on a multilingual corpus.
It initially lacks closed-loop environmental feedback, meaning it may not adapt to failures or changes in real-time without additional work.
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Recent data shows that SayCan has 5.4K monthly visits (63.3% increase from the previous month), 35.0% bounce rate, and average 2.48 pages per visit.
Traffic is primarily driven by 6 different sources, with users from 4 countries worldwide, led by United States contributing 60% of total traffic.
Monthly Visits
5.4K
Pages per Visit
2.48
Bounce Rate
35.0%
Average Time on Site
29s
| Source | Traffic Share |
|---|---|
| Direct | 54% |
| Search | 31% |
| Social | 7% |
| Referrals | 7% |
| Paid Referrals | 1% |
| Country Name | Traffic Share |
|---|---|
 | 60% |
 | 16% |
 | 12% |
 | 11% |
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