EVLP:Learning Unified Embodied Vision-Language Planner with Reinforced Supervised Fine-Tuning

• URL: http://arxiv.org/abs/2511.05553v1
• Date: Mon, 03 Nov 2025 10:24:49 GMT
• Title: EVLP:Learning Unified Embodied Vision-Language Planner with Reinforced Supervised Fine-Tuning
• Authors: Xinyan Cai, Shiguang Wu, Dafeng Chi, Yuzheng Zhuang, Xingyue Quan, Jianye Hao, Qiang Guan,
• Abstract summary: Current methods fail to adopt a unified generation framework for multimodal planning, lead to inconsistent in multimodal planning.<n>Our approach achieves multimodal planning for long-horizon tasks through a novel training pipeline incorporating dynamic pretraining and reinforced alignment.
• Score: 44.254412516852874
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In complex embodied long-horizon manipulation tasks, effective task decomposition and execution require synergistic integration of textual logical reasoning and visual-spatial imagination to ensure efficient and accurate operation. Current methods fail to adopt a unified generation framework for multimodal planning, lead to inconsistent in multimodal planning. To address this challenge, we present \textbf{EVLP (Embodied Vision-Language Planner)}, an innovative multimodal unified generation framework that jointly models linguistic reasoning and visual generation. Our approach achieves multimodal planning for long-horizon tasks through a novel training pipeline incorporating dynamic pretraining and reinforced alignment. Our core innovations consist of three key components: \textbf{1) Unified Multimodal Generation Framework}: For understanding, We integrate semantic information with spatial features to provide comprehensive visual perception. For generation, we directly learn the joint distribution of discrete images for one-step visual synthesis, enabling coordinated language-visual modeling through learnable cross-modal attention mechanisms. \textbf{2) Dynamic Perception Pretraining}: We propose a bidirectional dynamic alignment strategy employing inverse dynamics tasks and forward dynamics tasks, effectively strengthening multimodal correlations within a unified feature space. \textbf{3) Reinforced Supervised Fine-Tuning}: While conducting instruction-based fine-tuning in the unified generation space, we construct a reinforce loss to align the spatial logic between textual actions and generated images, enabling the model to acquire spatio-awared multimodal planning capabilities.

Related papers

• Beyond Language Modeling: An Exploration of Multimodal Pretraining [125.34714978184638]
  We provide empirical clarity through controlled, from-scratch pretraining experiments.<n>We adopt the Transfusion framework, using next-token prediction for language and diffusion for vision.<n>We demonstrate that the MoE architecture harmonizes this scaling asymmetry by providing the high model capacity required by language.
  arXiv  Detail & Related papers  (2026-03-03T18:58:00Z)
• U-Mind: A Unified Framework for Real-Time Multimodal Interaction with Audiovisual Generation [48.6868174403074]
  We introduce U-Mind, the first unified system for high-intelligence multimodal dialogue.<n>It supports real-time generation and jointly models language, speech, motion, and video synthesis within a single interactive loop.<n>We show that U-Mind achieves state-of-the-art performance on a range of multimodal interaction tasks.
  arXiv  Detail & Related papers  (2026-02-27T07:07:02Z)
• Synergizing Understanding and Generation with Interleaved Analyzing-Drafting Thinking [154.2388970262703]
  Unified Vision-Language Models (UVLMs) aim to advance multimodal learning by supporting both understanding and generation within a single framework.<n>We introduce the interleaved Analyzing-Drafting problem-solving loop (AD-Loop), a new think paradigm that alternates between analytic and drafting operations.<n>By interleaving textual thoughts with visual thoughts, AD-Loop enables models to iteratively refine both comprehension and outputs, fostering genuine synergy.
  arXiv  Detail & Related papers  (2026-02-24T23:26:09Z)
• Query-Kontext: An Unified Multimodal Model for Image Generation and Editing [53.765351127477224]
  Unified Multimodal Models (UMMs) have demonstrated remarkable performance in text-to-image generation (T2I) and editing (TI2I)<n>We introduce Query-Kontext, a novel approach that bridges the VLM and diffusion model via a multimodal kontext'' composed of semantic cues and coarse-grained image conditions encoded from multimodal inputs.<n> Experiments show that our approach matches strong unified baselines and even outperforms task-specific state-of-the-art methods in several cases.
  arXiv  Detail & Related papers  (2025-09-30T17:59:46Z)
• UniAlignment: Semantic Alignment for Unified Image Generation, Understanding, Manipulation and Perception [54.53657134205492]
  UniAlignment is a unified multimodal generation framework within a single diffusion transformer.<n>It incorporates both intrinsic-modal semantic alignment and cross-modal semantic alignment, thereby enhancing the model's cross-modal consistency and instruction-following robustness.<n>We present SemGen-Bench, a new benchmark specifically designed to evaluate multimodal semantic consistency under complex textual instructions.
  arXiv  Detail & Related papers  (2025-09-28T09:11:30Z)
• Training-Free Multimodal Large Language Model Orchestration [16.211979950149928]
  We report on an effective approach for creating interactive multimodal AI systems without additional training.<n>Our framework is built upon three key innovations: (1) a central controller that analyzes user inputs, (2) a parallel Text-to-Speech architecture, and (3) a crossmodal memory integration.
  arXiv  Detail & Related papers  (2025-08-06T16:17:29Z)
• Synergy-CLIP: Extending CLIP with Multi-modal Integration for Robust Representation Learning [2.56061946132533]
  We propose Synergy-CLIP, a framework that extends the contrastive language-image pre-training (CLIP) architecture to enhance multi-modal representation learning.<n>Unlike existing methods that focus on adapting individual modalities to vanilla-CLIP, Synergy-CLIP aligns and captures latent information across three modalities equally.<n>We introduce VGG-sound+, a triple-modal dataset designed to provide equal-scale representation of visual, textual, and audio data.
  arXiv  Detail & Related papers  (2025-04-30T07:14:58Z)
• Mastering Robot Manipulation with Multimodal Prompts through Pretraining and Multi-task Fine-tuning [49.92517970237088]
  We tackle the problem of training a robot to understand multimodal prompts. This type of task poses a major challenge to robots' capability to understand the interconnection and complementarity between vision and language signals. We introduce an effective framework that learns a policy to perform robot manipulation with multimodal prompts.
  arXiv  Detail & Related papers  (2023-10-14T22:24:58Z)
• Unified Discrete Diffusion for Simultaneous Vision-Language Generation [78.21352271140472]
  We present a unified multimodal generation model that can conduct both the "modality translation" and "multi-modality generation" tasks. Specifically, we unify the discrete diffusion process for multimodal signals by proposing a unified transition matrix. Our proposed method can perform comparably to the state-of-the-art solutions in various generation tasks.
  arXiv  Detail & Related papers  (2022-11-27T14:46:01Z)
• i-Code: An Integrative and Composable Multimodal Learning Framework [99.56065789066027]
  i-Code is a self-supervised pretraining framework where users may flexibly combine the modalities of vision, speech, and language into unified and general-purpose vector representations. The entire system is pretrained end-to-end with new objectives including masked modality unit modeling and cross-modality contrastive learning. Experimental results demonstrate how i-Code can outperform state-of-the-art techniques on five video understanding tasks and the GLUE NLP benchmark, improving by as much as 11%.
  arXiv  Detail & Related papers  (2022-05-03T23:38:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.