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EL3DD: Extended Latent 3D Diffusion for Language Conditioned Multitask ...
\tocauthorJonas Bode, Raphael Memmesheimer, Sven Behnke 11institutetext: 1 Autonomous Intelligent Systems, University of Bonn 11email: {bode, memmesheimer}@ais.uni-bonn.de, 11email: behnke@cs.uni-bonn.de home page: https://www.ais.uni-bonn.de/ 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. keywords: VLA, service robotics, manipulation, imitation learning 1 Introduction The development of general-purpose robots capable of adapting to human-centered environments has long been a goal in robotics, demanding advancements in perception, reasoning, and manipulation. Effective interaction within settings such as autonomous service or assistive robotics requires the ability to interpret natural language instructions and translate them into context-aware actions. Recent advances in the utilization of Large Language Models (LLMs) and Vision-Language Models (VLMs) to break down high-level natural language tasks into smaller steps are proving themselves to be increasingly viable [1, 12, 13]. However, while VLMs can assist in reasoning as well as the interpretation of natural language commands, they are insufficient to translate low-level steps such as ”Pick up the apple” into actionable trajectories. Through imitation learning, current advances are enabling robots to perform complex tasks by observing demonstrations and responding to new instructions in a zero-shot or few-shot fashion [19, 10, 6, 11, 2]. These Vision-Language-Action Models (VLAs) have been augmented by advances in computer vision, natural language p...
LaDiR: Latent Diffusion Enhances LLMs for Text Reasoning
Large Language Models (LLMs) demonstrate their reasoning ability through chain-of-thought (CoT) generation. However, LLM's autoregressive decoding may limit the ability to revisit and refine earlier tokens in a holistic manner, which can also lead to inefficient exploration for diverse solutions.
Beyond the next token: Why diffusion LLMs are changing the game
This article discusses the benefits of diffusion LLMs, a revolutionary approach to language models that offers a dynamic tradeoff between accuracy and performance. The article covers the architecture, evolution, and real-world statistics of this technology, including examples of open source models like LLaDA 2.X and Mercury 2.
Active learning for LLM-based recommender systems - Springer
To address these issues, this paper proposes an active learning framework for LLM-based recommender systems—ALLRec. This method evaluates the informativeness of training samples based on the loss values generated during the inference phase of LLMs, dynamically selecting key samples to improve training efficiency.
