VLA Model-Expert Collaboration for Bi-directional Manipulation Learning

The emergence of vision-language-action (VLA) models has given rise to foundation models for robot manipulation. Although these models have achieved significant improvements, their generalization in multi-task manipulation remains limited. This study proposes a VLA model-expert collaboration framework that leverages a limited number of expert actions to enhance VLA model performance. This approach reduces expert workload relative to manual operation while simultaneously improving the reliability and generalization of VLA models. Furthermore, manipulation data collected during collaboration can further refine the VLA model, while human participants concurrently enhance their skills. This bi-directional learning loop boosts the overall performance of the collaboration system. Experimental results across various VLA models demonstrate the effectiveness of the proposed system in collaborative manipulation and learning, as evidenced by improved success rates across tasks. Additionally, validation using a brain-computer interface (BCI) indicates that the collaboration system enhances the efficiency of low-speed action systems by involving VLA model during manipulation. These promising results pave the way for advancing human-robot interaction in the era of foundation models for robotics. (Project website: https://aoqunjin.github.io/Expert-VLA/)

Learning Novel Skills from Language-Generated Demonstrations

Current robot learning algorithms for acquiring novel skills often rely on demonstration datasets or environment interactions, resulting in high labor costs and potential safety risks. To address these challenges, this study proposes a skill-learning framework that enables robots to acquire novel skills from natural language instructions. The proposed pipeline leverages vision-language models to generate demonstration videos of novel skills, which are processed by an inverse dynamics model to extract actions from the unlabeled demonstrations. These actions are subsequently mapped to environmental contexts via imitation learning, enabling robots to learn new skills effectively. Experimental evaluations in the MetaWorld simulation environments demonstrate the pipeline's capability to generate high-fidelity and reliable demonstrations. Using the generated demonstrations, various skill learning algorithms achieve an accomplishment rate three times the original on novel tasks. These results highlight a novel approach to robot learning, offering a foundation for the intuitive and intelligent acquisition of novel robotic skills.