228M parameter edge-optimized Vision-Language-Action model. 49% smaller than SmolVLA, 47% faster, 17% better accuracy. Best efficiency/accuracy tradeoff. Trained on real-robot data.
EdgeVLA-Small combines FastViT-t8 vision with aggressive VLM layer pruning (16 to 8 layers) to achieve a 228M model that beats the 450M SmolVLA baseline on action prediction while running nearly 2x faster. The vision encoder is trained end-to-end — every parameter contributes at inference. Architecture inspired by DynamicVLA; VLM layer pruning is our contribution.
Trained exclusively on lerobot/fmb (3-camera Franka Panda manipulation). Source code: enfuse/edgevla
Intended Use & What You Can Do With This Model
This model predicts 7-DoF robot actions (x, y, z, rx, ry, rz, gripper) from 3 camera images. It outputs 50-step action chunks at 10Hz — each inference produces 5 seconds of continuous robot motion.
Immediate uses:
Deploy on a Franka Panda (or compatible 7-DoF arm) with a 3-camera setup for FMB-style tabletop manipulation. Feed camera frames in, execute the predicted delta actions.
Fine-tune on your own robot data — this is the most practical use. If you have any robot with cameras in LeRobot format, this checkpoint is an excellent pretrained starting point. Fine-tuning at LR=3e-5 for 50K steps typically adapts well to new setups.
Edge deployment — the best efficiency/accuracy tradeoff in the EdgeVLA family. Estimated ~184ms on Jetson Orin AGX with TensorRT FP16. At 228M params and 435MB FP16, it fits comfortably on Jetson Orin NX (16GB) and AGX (32–64GB).
Research baseline — 49% smaller than SmolVLA, nearly 2x faster, and still 17% better on action prediction.
Important caveats:
All metrics below are offline action prediction on held-out FMB samples. There are no closed-loop success rate numbers — the model has not been validated on a physical robot completing full tasks.
Trained specifically on FMB data (Franka Panda, specific manipulation tasks, 3-camera setup). It will not generalize to different robots, camera configurations, or tasks without fine-tuning.
The model expects 3 camera inputs (side_1, side_2, wrist). For single-camera setups, you would need to fine-tune with --empty_cameras or retrain.
