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

2025-11-17

AI Summary

This work addresses natural language–driven robotic manipulation for general-purpose robots operating in human environments. We propose a diffusion-based vision-language joint policy learning framework. Our method employs a language-conditioned 3D diffusion policy network that jointly models visual observations and textual instructions in a latent space. To enhance temporal consistency and semantic alignment for long-horizon, multi-step tasks, we introduce an improved multimodal embedding mechanism and a reference-example-guided training paradigm. Furthermore, we adapt techniques from image generation to optimize the diffusion process, thereby improving trajectory precision and cross-task generalization. Evaluated on the CALVIN benchmark, our approach achieves significant improvements over state-of-the-art baselines in both multi-task success rate and long-sequence execution stability. This work establishes a scalable, diffusion-based paradigm for grounding language into embodied action, advancing the frontier of language-to-action mapping in robotics.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Intelligent Robots: Manipulation

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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.

Problem

• Develop language-conditioned visuomotor policy for robots
• Generate precise robotic trajectories using diffusion models
• Enable multitask manipulation through sequential task execution

Innovation

• Extended latent 3D diffusion for robotic manipulation
• Combines visual and textual inputs for trajectory generation
• Leverages diffusion models and improved embeddings