Status
In Progress/Claimed
- Start point: Extend the isaac_so_arm101 repo (currently has Reach & Lift).
- New task: Add a Pick & Place example — grasp an object, move it, and release at a target location.
- What’s needed:
- Gripper meshes + articulation setup
- Reward shaping (grasp success, lifting, placing at target)
- Adjust observation space (e.g., object pose + end-effector pose)
- Outcome: Train with Isaac Lab’s RL libraries (e.g., rsl_rl, skrl) → validate in simulation.
- Why it matters: Builds a foundation for manipulation policies that go beyond simple reaching.
Video Flow:
- (2–3 minutes) Introduce yourself and the goal of the video/what will viewers learn.
- What does the current
isaac_so_arm101repo include? - Why is Pick and Place such an important benchmark for robotic arms?
- (3–4 minutes) How do you setup this project? What is the structure?
- Cover installation and cloning steps
- Explain the folder structure and key files.
- Briefly explain the role of each key file.
- (5–6 minutes) How do you implement the pick & place task?
- Show how to extend the existing environment: eg. adding a target platform, extending episode length, adding new rewards
- How do reward and termination functions guide the robot’s behavior?
- How does the central config file make experimentation easier?
- (4–5 minutes) How do you launch training and monitor progress?
- How do we know if the new environment was registered correctly and training is working?
- What happens during training, and how does the robot’s behavior evolve over time?
- What kinds of rewards or penalties shape how the robot learns to pick, move, and place?
- (4–5 minutes) What is transfer learning?
- What problem does transfer learning solve in reinforcement learning tasks?
- What do you gain from reusing the Lift model’s weights?
- What should you expect to observe in training after applying transfer learning?
- Explain and demo the script
Please include: