README.md

eval2_90

SmolVLA policy for Eval 2.

Dataset:

• robot-learning-group47/eval2_90_chengming_promptaug_v1

Training setup:

• Base policy: lerobot/smolvla_base
• One real camera: observation.images.front
• Camera mapping: observation.images.front → observation.images.camera1
• empty_cameras=2
• Dynamic prompt sampling: random prompt sampled within the same layout-target bucket during training
• n_obs_steps=1
• chunk_size=50
• n_action_steps=50
• Batch size: 64
• Steps: 30,000
• Save frequency: 2,500
• train_expert_only=true
• freeze_vision_encoder=true
• num_vlm_layers=16
• Learning rate: 1e-4
• Weight decay: 1e-10

The repo root contains the latest checkpoint for direct loading.

All intermediate checkpoints are stored under:

checkpoints/