CLIP-RT: Learning Language-Conditioned Robotic Policies from Natural Language Supervision

Teaching robots desired skills in real-world environments remains challenging, especially for non-experts. A key bottleneck is that collecting robotic data often requires expertise or specialized hardware, limiting accessibility and scalability. We posit that natural language offers an intuitive and accessible interface for robot learning. To this end, we study two aspects: (1) enabling non-experts to collect robotic data through natural language supervision (e.g., "move the arm to the right") and (2) learning robotic policies directly from this supervision. Specifically, we introduce a data collection framework that collects robot demonstrations based on natural language supervision and further augments these demonstrations. We then present CLIP-RT, a vision-language-action (VLA) model that learns language-conditioned visuomotor policies from this supervision. CLIP-RT adapts the pretrained CLIP models and learns to predict language-based motion primitives via contrastive imitation learning. We train CLIP-RT on the Open X-Embodiment dataset and finetune it on in-domain data collected by our framework to learn diverse skills. CLIP-RT demonstrates strong capabilities in learning novel manipulation skills, outperforming the state-of-the-art model, OpenVLA (7B parameters), by 24% in average success rates, while using 7x fewer parameters (1B). We further observe that CLIP-RT shows significant improvements in few-shot generalization. Finally, through collaboration with humans or large pretrained models, we demonstrate that CLIP-RT can further improve its generalization on challenging robotic tasks.