Florian Shkurti

SC3-Eval achieves a remarkable 0.929 Pearson correlation in evaluating robot policies, revealing critical insights into their real-world performance.

DEHP dramatically boosts the success rates of high-precision robotic tasks by dynamically adjusting execution horizons based on task complexity.

Torque Adaptation Module enables zero-shot robust manipulation across different robots without the need for extensive retraining or domain randomization.

No more performance cliffs: SMAC lets you smoothly fine-tune offline RL policies online by aligning policy gradients with Q-function gradients.

Simply propagating uncertainty from perception modules to motion planning can significantly improve the generalization capabilities of autonomous vehicles in complex, closed-loop scenarios.