typo fix

src/pages/index.mdx

@@ -151,7 +151,7 @@ During online training, we fine-tune <LaTeX inline formula="E_{\theta}"/> using
 
 ## Experimental Evaluation
 We evaluate the effectiveness with which P<span style="font-size: 80%">ROGRESSOR</span> learns reward functions from visual demonstrations that enable robots to perform various manipulation tasks in simulation as well as the real world.
-<Figure caption="Visualization of the robotic tasks: (a-d) Real world environments with a UR5 arm. (e-j) Simulation environments for evaluation using the Meta-World benchmark">
+<Figure caption="Visualization of the robotic tasks: (a-d) Real world environments with a UR5 arm. (e-j) Simulation environments for evaluation using the Meta-World benchmark.">
 
     <Image source={expfig} altText="Visualization of the robotic tasks" />
 </Figure>
@@ -172,7 +172,7 @@ In our simulated experiments, we used benchmark tasks from the Meta-World enviro
 <Figure caption="Visualization of policy learning in the Meta-World simulation environment. We run PROGRESSOR and several baselines on
 six diverse tasks of various difficulties. We also run PROGRESSOR without online push-back as an ablation. We report the environment
 reward during training (left) and the task success rate from 10 rollouts (right) averaged over five seeds. The solid line denotes the mean and
-the transparent area denotes standard deviation. PROGRESSOR demonstrates clear advantages in both metrics, especially at early stages of training">
+the transparent area denotes standard deviation. PROGRESSOR demonstrates clear advantages in both metrics, especially at early stages of training.">
 
 <Image source={legend} altText="legend" />
 </Figure>