Isaac Lab and robot simulation

Go beyond running a new simulator example: Identify the observations, actions, and physics that your research task requires you to change.

• CPU path: Gymnasium, NumPy, and Matplotlib.
• Official SO-101 tutorial: Python 3.12, uv, Git LFS, Linux/Windows + NVIDIA GPU.
• GPU environment: Check before class. Prefer personal equipment, supplemented by shared resources.
• Jetson extension: edge inference and latency measurement.

Mobile robot paths and safety

An application design case choosing either grid paths or continuous velocity control.

Observation·State

Position, target-relative position, and nearby obstacle observations.

Action

Grid direction selection or bounded linear and angular velocities.

Reward·Constraint

Separate arrival, collision, and movement costs. Distinguish collisions from timeouts.

Comparison baseline

Shortest path and rule-based avoidance.

Metrics to check

Success rate, collision rate, travel distance, and time. Evaluate on unseen maps.

Drive-system tracking performance and input cost

A synthetic 1st-order motor model that tracks a constant reference angular velocity in each experiment. Separate training at target 1 from evaluation at 0.5, 1, and 1.5.

Observation·State

Angular velocity $\omega/5$, target $r/2$.

Action

Continuous $a\in[-1,1]$, actual input $u=2a$.

Reward·Constraint

$-((r-\omega)^2+0.05u^2)$. Termination at $|\omega|>5$; time limit at 100 steps.

Comparison baseline

P control with the same input limits.

Metrics to check

Tracking RMSE, mean u², and number of safety terminations. Evaluate separately for each target.

Starting conditions

Start from CartPole-v1 observations, actions, resets, and time limits.

Procedure

1. Run the provided rule policy and record observations and actions for one episode.
2. In the official Isaac Lab Quickstart, identify and map the roles of the task, physics backend, renderer, and RL library.

Results to retain

• Environment interface mapping table and one rollout.

Starting conditions

GPU environment: On a prepared Linux/Windows NVIDIA system, follow the official Quickstart in this order: task list → zero/random agent → training → play. CPU environment: During the same session, complete CartPole transition and policy comparisons and task configuration mapping.

Procedure

1. If a GPU environment is ready, run the Cartpole task following the current official documentation, and record the task ID, backend, seed, and execution command.
2. If no GPU environment is available, compare Random and Rule trajectories with the provided CPU code and identify termination causes.
3. In your selected path, find the setting to modify to change one observation, action, or termination rule.

Results to retain

• GPU: Run screenshot or rollout and execution conditions. CPU: State trajectory plot and configuration mapping table.

Starting conditions

Choose manipulation, mobile robotics, or process control.

Procedure

1. Tabulate the scene, state observations, action scale, reward, reset, and termination.
2. Link to the files and components containing these settings in the official Isaac Lab robot tutorial.
3. Decide what to hold fixed and which single item to change in the first experiment.

Results to retain

• Minimum task design table and initial validation plan.

• Isaac Lab · Quickstart
• Isaac Lab · SO-101 task tutorial