step 18 000 of a planned 20 000 — stopped early, see below
Final loss
0.053 at step 16 000 (0.054 at 18 000)
Trained on
Jetson Orin Nano Super, 8 GB unified memory, torch 2.8.0 + CUDA 12.6
Batch size / throughput
2 · ~1.05 step/s · 4 h 54 m wall clock
Cameras
head → camera1, right_arm_wrist → camera2, left_arm_wrist → camera3
Action / state
17-dim (left arm 6 · right arm 6 · head 2 · base 3)
chunk_size / n_action_steps
50 / 50
Training data: 40 of the 50 episodes
dataset.eval_split=0.2 held out the last 10 episodes. Because the dataset has a single task,
LeRobot's per-task split (datasets/factory.py:156) took the last 10 episodes — which, since
cup positions were recorded in order, is exactly position P5.
So this model trained on P1–P4 (40 episodes / 15 866 frames) and never saw P5.
⚠️ eval_steps was left at its default of 0, and lerobot_train.py:748 gates evaluation on
cfg.eval_steps > 0. No validation ran during training. It was measured afterwards instead —
see below.
Held-out result: no position memorisation detected
Measured after training with 05-training/eval_heldout.py, 120 frames per group, using the same
loss path as training (uint8→float32/255 → preprocessor → policy(batch)):
frames
loss
Train positions P1–P4
120
0.2348
Held-out P5 (never trained on)
120
0.2300
ratio 0.98×
The model does as well on a position it never saw as on the ones it trained on. Had it merely
memorised "P3 → this trajectory", the held-out loss would be several times higher. This is the
pretrained prior doing its job.
This does not replace a real-robot test. P5 is still the same table, cup and lighting; a low
loss says the predicted actions match the human demonstrations, not that the arm actually grasps.
Why training stopped at 18 000 instead of 20 000
Not divergence, not a manual interrupt — the disk filled up while writing the step-18 000
checkpoint:
safetensors._safetensors_rust.SafetensorError: Error while serializing:
I/O error: No space left on device (os error 28)
Each SmolVLA checkpoint is 1.26 GB and one was saved every 2 000 steps. The step-18 000 checkpoint
was left half-written, so checkpoints/last still points at 016000 — which is what is uploaded
here. Loss had already flattened (0.053 at 16 000, 0.054 at 18 000), so the missing 2 000 steps
make no material difference.
Loss curve
training_log.csv in this repo carries all 27 logged points. Summary:
step
epoch
loss
grad norm
100
0.01
2.366
18.683
200
0.03
0.936
8.605
16 000
2.08
0.053
1.178
18 000
2.27
0.054
1.223
Loss fell 98% and the gradient norm 93%; the last 2 000 steps vary by less than 0.02. Converged.
Limitations
Cannot drive the left arm. The recording ran with arms: 1.0, so action dimensions 0–5 hold
one constant across all frames. This policy will not transfer to a left-arm version of the task.
Trained on data from the 68 mm gripper. The Fin-Ray blade is being replaced with a 90 mm
version; lever arm and compliance both change, so real-world behaviour must be re-measured.
Zero physical trials so far. Every number here is offline.
Companion model
Suyang99/xlerobot-act-cup-grasp-right
— ACT trained from scratch on all 50 episodes, for comparison. Loss values are not comparable
between the two: ACT's is l1 + 10 × kld, SmolVLA's is a different objective. Only real-robot
success rate compares them.
