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EL3DD: Extended Latent 3D Diffusion for Language Conditioned Multitask Manipulation

Jonas Bode, Raphael Memmesheimer, Sven Behnke

TL;DR

This paper tackles language-conditioned multitask manipulation for robots by extending a diffusion-based visuomotor policy. It introduces EL3DD, which combines an enhanced language representation (S-BERT), per-pixel visual embeddings (LSeg), and latent diffusion to generate end-effector trajectories. Evaluated on the CALVIN benchmark, EL3DD achieves state-of-the-art long-horizon performance and robustness across sequential tasks, highlighting diffusion models as a strong foundation for VLAs. While demonstrating clear benefits, the work acknowledges CALVIN-specific limitations and calls for broader datasets and real-world validation to ensure generalization and deployment viability.

Abstract

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.

EL3DD: Extended Latent 3D Diffusion for Language Conditioned Multitask Manipulation

TL;DR

This paper tackles language-conditioned multitask manipulation for robots by extending a diffusion-based visuomotor policy. It introduces EL3DD, which combines an enhanced language representation (S-BERT), per-pixel visual embeddings (LSeg), and latent diffusion to generate end-effector trajectories. Evaluated on the CALVIN benchmark, EL3DD achieves state-of-the-art long-horizon performance and robustness across sequential tasks, highlighting diffusion models as a strong foundation for VLAs. While demonstrating clear benefits, the work acknowledges CALVIN-specific limitations and calls for broader datasets and real-world validation to ensure generalization and deployment viability.

Abstract

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.

Paper Structure

This paper contains 14 sections, 7 equations, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Works
  3. Language-Conditioned Multitask Manipulation
  4. Manipulation Using Diffusion Models
  5. Methodology
  6. 3DDA
  7. Improvements
  8. Additional Semantic Information
  9. LSeg for Visual Embeddings
  10. Latent Diffusion
  11. Experiments & Results
  12. Dataset
  13. Ablation Study
  14. Conclusion

Figures (2)

  • Figure 1: Overview of our EL3DD architecture. Compared to 3DDA 3dda we replace the CLIP Image encoder with an LSeg Lseg image encoder, add an additional semantic S-BERT SentenceBert encoding, and expand the denoising transformer, which generates the end-effector trajectories into an LDM. The Figure shows input data in green, output data in red, components carried over from 3DDA in orange and new components in blue.
  • Figure 2: EL3DD executing a task chain during evaluation. All 5 tasks were successfully executed in a row.