To bridge the gap between vision-language (VL) and embodied acting tasks for more seamless integration, several strategies can be employed:
Multi-modal Representation: Develop more advanced multi-modal representation models that can effectively fuse visual, textual, and action information. This can help in creating a unified understanding of the environment and tasks.
Embodied Action Prediction: Enhance the capability of models to predict and generate embodied actions based on the visual and textual inputs. This involves training models to understand the spatial relations in the 3D world and generate appropriate actions.
Fine-tuning with Embodied Tasks: Incorporate more diverse and complex embodied tasks during the fine-tuning stage of the model. This exposure to a wide range of tasks can help the model generalize better and perform more effectively in real-world scenarios.
Data Augmentation: Augment the training data with a variety of scenarios, objects, and actions to expose the model to a broader range of possibilities. This can help in improving the model's ability to generalize and adapt to new situations.
Continuous Learning: Implement a continuous learning framework where the model can adapt and improve over time based on new experiences and feedback. This can help in refining the model's performance and addressing any shortcomings in its understanding of the 3D world.