ActionMap (50 demos per task)

We replaced only OpenVLA-OFT’s L1 regression head with the paper’s voxel heatmap head, and trained both on the same 50 demonstrations per task with the paper’s real-robot recipe. Both heads match the official code. This round evaluates them on the station’s synchronous runtime. Differences from the paper:

• Two arms and three cameras. Each step predicts 14 action values instead of 7, and ActionMap has one heatmap branch per arm on a shared trunk.
• Delta actions on absolute targets. Both heads predict per-step deltas that are added to the measured pose, with normalization ranges fitted per chunk position. Rotations are rotation vectors instead of Euler angles.
• Scarce data. Each task’s 50 training episodes are a fixed, seeded subset of the 276 to 288 available demonstrations, stratified by which arm moves.
• Training budget. Both heads train for 4,000 steps at a constant learning rate of 5e-4 and batch 64, without a validation split.
• Chunk timing and execution. Chunks are 16 steps at 30 Hz, about the paper’s 0.5 s horizon, and each chunk executes in full before the next request, as in OFT.
• Evaluation. Each task runs 30 placements with one trial each, instead of the paper’s 10 trials.
• Hardware. The pour pair trained on four A100s and the other pairs on four H100s, always the same within a task.