small typo

content/_index.md

@@ -49,7 +49,7 @@ Given a dynamics predictor and a small calibration dataset, LUCCa provides proba
 
 # Experiments
 
-We conducted experiments with an MPC controller that uses LUCCa to plan short-horizon trajectories to reach a goal region (without colliding with any obstacles). We compared LUCCa with a baseline (same predictor but without the conformal calibration step) on a double-integrator system over four environments shown below. In the white regions the dynamics predictor corresponds to the ground truth dynamics, but in the yellow regions there is a significant mismatch (actual dynamics become lower-friction). In both areas there is aleatoric uncertainty. This dynamical system can build significant momentum, and thus underestimating the predictive uncertainty can lead to entering regions of inevitable collision. Hence, it is crucial to accurately quantity uncertainty multiple time-steps into the future. 
+We conducted experiments with an MPC controller that uses LUCCa to plan short-horizon trajectories to reach a goal region (without colliding with any obstacles). We compared LUCCa with a baseline (same predictor but without the conformal calibration step) on a double-integrator system over four environments shown below. In the white regions the dynamics predictor corresponds to the ground truth dynamics, but in the yellow regions there is a significant mismatch (actual dynamics become lower-friction). In both areas there is aleatoric uncertainty. This dynamical system can build significant momentum, and thus underestimating the predictive uncertainty can lead to entering regions of inevitable collision. Hence, it is crucial to accurately quantify uncertainty multiple time-steps into the future. 
 
 {{ figure(src = ["./Corridor_Uncalibrated Baseline.mp4","./Corridor_LUCCa.mp4"], subcaption = ["**Uncalibrated Baseline** (Corridor Map)","**LUCCa** (Corridor Map)"], dark_invert=[false,false]) }}