Closed-Loop Neural Activation Control in Vision-Language-Action Models

In three linesCTRL-STEER introduces a closed-loop control framework for Vision-Language-Action (VLA) models. Instead of fixed steering coefficients, it adaptively adjusts intervention strength over time using PID or reinforcement learning controllers. Experiments on OpenVLA with LIBERO task suites demonstrate improved concept regulation stability and better steering-task success trade-offs without retraining the base model.

Summary generated by Claude — human-verified