Unverified Commit 161ef855 authored by njawale42's avatar njawale42 Committed by GitHub

Updates SkillGen documentation for data gen command and success rates (#3702)

# SkillGen documentation: data gen command cleanup and success-rate
guidance

## Description

This PR updates the SkillGen documentation in
`docs/source/overview/imitation-learning/skillgen.rst`:
- Removes a redundant `--headless` flag from a data generation command
example.
- Adds a note with success-rate guidelines and training recommendations
for cube stacking and bin cube stacking.
- Clarifies minor text details, including correcting the planning phase
order to “Retreat → Contact → Approach” for consistency.

Motivation: Improve clarity, set realistic expectations on data
generation and downstream policy performance, and align docs with actual
planner behavior.

Dependencies: None

## Type of change

- Documentation update

## Checklist

- [x] I have read and understood the [contribution
guidelines](https://isaac-sim.github.io/IsaacLab/main/source/refs/contributing.html)
- [x] I have run the [`pre-commit` checks](https://pre-commit.com/) with
`./isaaclab.sh --format`
- [x] I have made corresponding changes to the documentation
- [x] My changes generate no new warnings
- [ ] I have added tests that prove my fix is effective or that my
feature works
- [ ] I have updated the changelog and the corresponding version in the
extension's `config/extension.toml` file
- [x] I have added my name to the `CONTRIBUTORS.md` or my name already
exists there
parent a77910ba
...@@ -280,8 +280,7 @@ Once satisfied with small-scale results, generate a full training dataset: ...@@ -280,8 +280,7 @@ Once satisfied with small-scale results, generate a full training dataset:
--input_file ./datasets/annotated_dataset_skillgen.hdf5 \ --input_file ./datasets/annotated_dataset_skillgen.hdf5 \
--output_file ./datasets/generated_dataset_skillgen_cube_stack.hdf5 \ --output_file ./datasets/generated_dataset_skillgen_cube_stack.hdf5 \
--task Isaac-Stack-Cube-Franka-IK-Rel-Skillgen-v0 \ --task Isaac-Stack-Cube-Franka-IK-Rel-Skillgen-v0 \
--use_skillgen \ --use_skillgen
--headless
.. note:: .. note::
...@@ -357,8 +356,8 @@ Train a state-based policy for the basic cube stacking task: ...@@ -357,8 +356,8 @@ Train a state-based policy for the basic cube stacking task:
--algo bc \ --algo bc \
--dataset ./datasets/generated_dataset_skillgen_cube_stack.hdf5 --dataset ./datasets/generated_dataset_skillgen_cube_stack.hdf5
Adaptive Bin Stacking Policy Adaptive Bin Cube Stacking Policy
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Train a policy for the more complex adaptive bin stacking: Train a policy for the more complex adaptive bin stacking:
...@@ -374,7 +373,7 @@ Train a policy for the more complex adaptive bin stacking: ...@@ -374,7 +373,7 @@ Train a policy for the more complex adaptive bin stacking:
The training script will save the model checkpoints in the model directory under ``IssacLab/logs/robomimic``. The training script will save the model checkpoints in the model directory under ``IssacLab/logs/robomimic``.
Evaluating Trained Policies Evaluating Trained Policies
^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^^^^^^^^^^^
Test your trained policies: Test your trained policies:
...@@ -389,13 +388,22 @@ Test your trained policies: ...@@ -389,13 +388,22 @@ Test your trained policies:
.. code:: bash .. code:: bash
# Adaptive bin stacking evaluation # Adaptive bin cube stacking evaluation
./isaaclab.sh -p scripts/imitation_learning/robomimic/play.py \ ./isaaclab.sh -p scripts/imitation_learning/robomimic/play.py \
--device cpu \ --device cpu \
--task Isaac-Stack-Cube-Bin-Franka-IK-Rel-Mimic-v0 \ --task Isaac-Stack-Cube-Bin-Franka-IK-Rel-Mimic-v0 \
--num_rollouts 50 \ --num_rollouts 50 \
--checkpoint /path/to/model_checkpoint.pth --checkpoint /path/to/model_checkpoint.pth
.. note::
**Expected Success Rates and Recommendations for Cube Stacking and Bin Cube Stacking Tasks**
* SkillGen data generation and downstream policy success are sensitive to the task and the quality of dataset annotation, and can show high variance.
* For cube stacking and bin cube stacking, data generation success is typically 40% to 70% when the dataset is properly annotated per the instructions.
* Behavior Cloning (BC) policy success from 1000 generated demonstrations trained for 2000 epochs (default) is typically 40% to 85% for these tasks, depending on data quality.
* Recommendation: Train for the default 2000 epochs with about 1000 generated demonstrations, and evaluate multiple checkpoints saved after the 1000th epoch to select the best-performing policy.
.. _cuRobo-interface-features: .. _cuRobo-interface-features:
cuRobo Interface Features cuRobo Interface Features
...@@ -416,9 +424,9 @@ cuRobo Planner (GPU, collision-aware) ...@@ -416,9 +424,9 @@ cuRobo Planner (GPU, collision-aware)
* Location: ``isaaclab_mimic/motion_planners/curobo`` * Location: ``isaaclab_mimic/motion_planners/curobo``
* Multi-phase planning: * Multi-phase planning:
* Approach → Contact → Retreat phases per subtask * Retreat → Contact → Approach phases per subtask
* Configurable collision filtering in contact phases * Configurable collision filtering in contact phases
* For SkillGen, approach and retreat phases are collision-free. The transit phase is collision-checked. * For SkillGen, retreat and approach phases are collision-free. The transit phase is collision-checked.
* World synchronization: * World synchronization:
......
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