Refine content for DGX Spark robotics learning path

Updated the title and descriptions for clarity and consistency. Revised learning objectives and prerequisites for better readability.

Author: pareenaverma

content/learning-paths/laptops-and-desktops/dgx_spark_isaac_robotics/_index.md

@@ -1,26 +1,25 @@
 ---
-title: Build Robot Simulation and RL Workflows with Isaac Sim and Isaac Lab on DGX Spark
+title: Build Robot Simulation and Reinforcement Learning Workflows with Isaac Sim and Isaac Lab on DGX Spark
 
 draft: true
 cascade:
     draft: true
 
 minutes_to_complete: 90
 
-who_is_this_for: This learning path is intended for robotics developers, simulation engineers, and AI researchers who want to run high-fidelity robotic simulations and reinforcement learning (RL) pipelines using Isaac Sim and Isaac Lab on Arm-based NVIDIA DGX Spark systems powered by the Grace–Blackwell (GB10) architecture.
+who_is_this_for: This learning path is intended for robotics developers, simulation engineers, and AI researchers who want to run high-fidelity robotic simulations and reinforcement learning (RL) pipelines using NVIDIA Isaac Sim and Isaac Lab on Arm-based NVIDIA DGX Spark system powered by the Grace–Blackwell (GB10) architecture.
 
 learning_objectives:
-    - Explain the roles of Isaac Sim and Isaac Lab, and describe how DGX Spark accelerates robotic simulation and reinforcement learning workloads
-    - Build Isaac Sim and Isaac Lab from source on an Arm-based DGX Spark system
-    - Launch and control a basic robot simulation in Isaac Sim using Python scripts
-    - Train and evaluate a reinforcement learning policy for the Unitree H1 humanoid robot using Isaac Lab and the RSL-RL interface
+    - Describe the roles of Isaac Sim and Isaac Lab within a robotics simulation and RL pipeline
+    - Build and configure Isaac Sim and Isaac Lab on an Arm-based DGX Spark system
+    - Launch and control a robot simulation in Isaac Sim using Python
+    - rain and evaluate a reinforcement learning policy for the Unitree H1 humanoid robot using Isaac Lab and RSL-RL
 
 prerequisites:
-    - Access to an NVIDIA DGX Spark system with at least 50 GB of free disk space
+    - A NVIDIA DGX Spark system with at least 50 GB of free disk space
     - Familiarity with Linux command-line tools
     - Experience with Python scripting and virtual environments
     - Basic understanding of reinforcement learning concepts (rewards, policies, episodes)
-    - Experience building software from source using CMake and make
 
 author:
     - Johnny Nunez