Simplevla Rl

Introducing SimpleVLA-RL — using simple binary 0/1 rewards with RL to improve VLA models by 430%, no reward engineering needed.

Deep-dive into OpenVLA-OFT backbone, GRPO optimizer, dynamic sampling, and the exploration mechanisms that let VLA models self-improve.

Step-by-step guide to setting up the environment, running SFT cold-start, and RL training for SimpleVLA-RL on LIBERO and RoboTwin.

Analyzing SimpleVLA-RL results: ablation studies, the pushcut phenomenon, real-world transfer, and 5 key lessons learned.

In-depth comparison of SimpleVLA-RL and LeRobot: RL approach, VLA models, sim vs real, data efficiency — two complementary frameworks.

A detailed analysis of training the 7-DoF OpenArm robot for carton box grasping — comparing 3 paths: LeRobot native, SimpleVLA-RL style, and hybrid.

Step-by-step guide to setting up OpenArm, calibrating, teleoperating, and collecting 50 box-grasping episodes with LeRobot.

Train SmolVLA, ACT, Pi0-FAST for OpenArm box grasping — from fine-tuning to real robot deployment and improvement with HIL-SERL.

Set up OpenArm in Isaac Lab, collect demonstration data in simulation, and convert to SimpleVLA-RL training format.

Step-by-step guide to SFT fine-tuning and RL training with SimpleVLA-RL for OpenArm — from environment config to running GRPO.

Deploy a SimpleVLA-RL model from simulation to real OpenArm — camera setup, action mapping, and tips for reducing the sim-to-real gap.