EL3DD: Extended Latent 3D Diffusion for Language Conditioned Multitask Manipulation
This work addresses the challenge of general-purpose robots acting in human environments through language-conditioned manipulation, though it appears incremental as it extends existing models with improved embeddings and adapted techniques.
The paper tackles the problem of enabling robots to understand natural language commands and execute physical manipulation tasks by developing a visuomotor policy framework that combines visual and textual inputs using diffusion models. The result is enhanced performance on manipulation tasks in the CALVIN dataset, with an increased long-horizon success rate for sequential tasks.
Acting in human environments is a crucial capability for general-purpose robots, necessitating a robust understanding of natural language and its application to physical tasks. This paper seeks to harness the capabilities of diffusion models within a visuomotor policy framework that merges visual and textual inputs to generate precise robotic trajectories. By employing reference demonstrations during training, the model learns to execute manipulation tasks specified through textual commands within the robot's immediate environment. The proposed research aims to extend an existing model by leveraging improved embeddings, and adapting techniques from diffusion models for image generation. We evaluate our methods on the CALVIN dataset, proving enhanced performance on various manipulation tasks and an increased long-horizon success rate when multiple tasks are executed in sequence. Our approach reinforces the usefulness of diffusion models and contributes towards general multitask manipulation.