CAREER: Introspective Reasoning with Imprecise Models for Reliable Autonomy — NSF Award to Oregon State University (OR, $340,412)
The real world is too complex to model accurately. Autonomous agents and robots that perform complex tasks in the real world, ranging from handling inventory in warehouses to driving, will inevitably encounter scenarios that are not fully described in their symbolic models used for decision-making. To handle such unexp
| Award title | CAREER: Introspective Reasoning with Imprecise Models for Reliable Autonomy |
|---|---|
| Award ID | 2543646 |
| Awardee | Oregon State University |
| City | CORVALLIS |
| State | OR |
| Amount obligated | $340,412 |
| Principal investigator | Sandhya Saisubramanian |
| Program | Robust Intelligence |
| Start date | 09/01/2026 |
| Abstract | The real world is too complex to model accurately. Autonomous agents and robots that perform complex tasks in the real world, ranging from handling inventory in warehouses to driving, will inevitably encounter scenarios that are not fully described in their symbolic models used for decision-making. To handle such unexpected scenarios, agents often rely on human assistance to complete the task, restore safety, or refine the model. While these interventions can restore safety in the short term, th |
| Source | NSF Awards |
$799/mo
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