RICL for LIBERO - Implementation Summary

Overview

Successfully implemented RICL (Retrieval-Augmented In-Context Learning) training pipeline for the LIBERO dataset using the ricl_openpi codebase (JAX/Flax). The implementation strictly follows VLA-Humanoid's approach while adapting to ricl_openpi's architecture.

Implementation Status

✅ Phase 1: Analysis and Planning

Analyzed LIBERO dataset structure (LeRobot format with parquet files)
Studied VLA-Humanoid's RICL implementation (PyTorch-based)
Examined ricl_openpi architecture (JAX/Flax-based)
Identified key differences and adaptation requirements
Created detailed implementation plan

✅ Phase 2: Data Preparation

Files Created:

src/openpi/data/ricl_libero_dataset.py - LIBERO dataset loader
scripts/build_ricl_context_libero.py - DINOv2 embedding + FAISS index builder
slurm/precompute_ricl_context_libero.slurm - SLURM script for precomputation
scripts/test_ricl_data_pipeline.py - Data pipeline test script

Key Features:

Loads parquet files from LIBERO dataset
Extracts 6-dim state (observation.states.ee_state) and 7-dim action
Builds action chunks of size 50
Loads video frames from MP4 files
Builds DINOv2 embeddings (768-dim) for all frames
Creates FAISS index for fast nearest neighbor retrieval
Pre-computes NN indices/distances for training efficiency
Self-exclusion in retrieval (no frame retrieves itself)

Test Results:

✓ All data pipeline tests passed
✓ Tested on merged_libero_mask_depth_noops_lerobot_10 (200 episodes)
✓ State shape: (6,), Action shape: (7,)
✓ DINOv2 encoding confirmed working
✓ Video loading confirmed working

✅ Phase 3: Training Script Development

Files Modified/Created:

src/openpi/training/data_loader.py - Integrated LIBERO dataset support
slurm/train_ricl_libero.slurm - SLURM training script

RICL Training Details:

Model: Pi0FASTRicl (already existed in ricl_openpi)
Backbone: Gemma 2B with SigLIP vision encoder
RICL Logic: Already implemented in Pi0FASTRicl.compute_loss()
- Retrieves top-K demos based on DINOv2 embeddings
- Concatenates demo + query observations
- Computes interpolated target: w * a_nn + (1-w) * a_policy
- Weight: w = exp(-lambda * L2_distance)
Action Interpolation: Implemented at token level (discrete interpolation)
Hyperparameters:
- Lambda decay: 10.0 (controls interpolation weight)
- Top-K: 1 (only supports K=1 currently)
- Action horizon: 50
- Batch size: 4 (test, can scale up)
- Learning rate: 1e-4

⏳ Phase 4: Evaluation (TODO)

Not yet implemented. Will need to:

Adapt ricl_eval.py for LIBERO environment
Create evaluation SLURM script
Test on LIBERO tasks

⏳ Phase 5: Verification (TODO)

Train model for few steps
Verify loss decreases
Check retrieval quality
Full evaluation

Dataset Configuration

Test Dataset (Current)

Path: merged_libero_mask_depth_noops_lerobot_10
Size: 200 episodes, ~40k frames
Tasks: libero_10 suite (10 tasks x 20 demos each)

Full Dataset (Future)

Path: merged_libero_mask_noops_lerobot_v4
Size: ~400 episodes, ~80k frames
Tasks: All LIBERO suites (libero_10, libero_spatial, libero_object, libero_goal)

Architecture Notes

ricl_openpi vs VLA-Humanoid

Aspect	VLA-Humanoid	ricl_openpi
Framework	PyTorch	JAX/Flax
Model	PI0	OpenPI (Gemma-based)
Data	LeRobot format	LeRobot format
RICL	Custom implementation	Built-in Pi0FASTRicl
Normalization	`normalize_inputs/targets`	Transform pipeline
Training	Accelerate	JAX native distributed

Key Adaptation Challenges

Data Loading: Had to integrate LIBERO dataset into existing JAX data pipeline
State Dimension: LIBERO uses 6-dim ee_state, not the expected 8-dim
Video Loading: Videos stored in chunk-based directory structure
Model Architecture: ricl_openpi already had RICL implementation, just needed data

Next Steps

Immediate (User Action Required)

Run precomputation:
```
sbatch slurm/precompute_ricl_context_libero.slurm
```
Wait for completion (~30-60 mins)
Run training:
```
sbatch slurm/train_ricl_libero.slurm
```

Future Improvements

Evaluation: Implement LIBERO evaluation script
Multi-Demo Retrieval: Support K > 1 retrieval
Scale Up: Train on full LIBERO dataset
Ablations: Test different lambda values, retrieval strategies
Normalization: Verify normalization matches original exactly

Files Reference

Core Implementation

in_context_learning/ricl_openpi/
├── src/openpi/
│   ├── data/
│   │   └── ricl_libero_dataset.py          # LIBERO dataset loader
│   ├── training/
│   │   └── data_loader.py                  # Modified: LIBERO integration
│   └── models/
│       └── pi0_fast_ricl.py                # RICL model (pre-existing)
├── scripts/
│   ├── build_ricl_context_libero.py        # Precomputation script
│   ├── test_ricl_data_pipeline.py          # Test script
│   └── train_pi0_fast_ricl.py              # Training script (pre-existing)
├── slurm/
│   ├── precompute_ricl_context_libero.slurm  # Precompute SLURM
│   └── train_ricl_libero.slurm               # Training SLURM
├── RICL_LIBERO_QUICKSTART.md               # Quick start guide
└── rag/
    └── ricl_training_context_libero_10_test/  # Output directory for context

Documentation

RICL_LIBERO_QUICKSTART.md - Quick start instructions
implementation_plan.md - Detailed implementation plan (in artifacts)
task.md - Task checklist (in artifacts)

References

Original RICL paper: https://arxiv.org/abs/2312.11805
VLA-Humanoid repo: (internal)
ricl_openpi repo: (internal)
LIBERO dataset: https://libero-project.github.io/