DEED

DEED framework bridges lab-to-store gap for humanoid retail robots using data-efficient VLA post-training

Researchers present DEED (Data-Efficient Post-Training and Experience-Driven Learning), a systems-level framework for deploying Vision-Language-Action humanoid robots in real retail environments. The approach is evaluated on a supermarket chip-restocking task using a Unitree G1-Edu humanoid and NVIDIA's GR00T N1.6 foundation model, combining data-efficient post-training, experience-driven refinement adapted from RECAP, and a latent-space analysis tool. Key finding: bridging lab-to-real performance gaps is primarily a systems integration challenge rather than an architectural one, achievable with careful data design on a single GPU.