Image Generation as a Visual Planner for Robotic Manipulation
- URL: http://arxiv.org/abs/2512.00532v1
- Date: Sat, 29 Nov 2025 15:54:16 GMT
- Title: Image Generation as a Visual Planner for Robotic Manipulation
- Authors: Ye Pang,
- Abstract summary: Generating realistic robotic manipulation videos is an important step toward unifying perception, planning, and action in embodied agents.<n>We propose a two-part framework that includes: (1) text-conditioned generation, which uses a language instruction and the first frame, and (2) trajectory-conditioned generation, which uses a 2D trajectory overlay and the same initial frame.<n>Our findings indicate that pretrained image generators encode transferable temporal priors and can function as video-like robotic planners under minimal supervision.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Generating realistic robotic manipulation videos is an important step toward unifying perception, planning, and action in embodied agents. While existing video diffusion models require large domain-specific datasets and struggle to generalize, recent image generation models trained on language-image corpora exhibit strong compositionality, including the ability to synthesize temporally coherent grid images. This suggests a latent capacity for video-like generation even without explicit temporal modeling. We explore whether such models can serve as visual planners for robots when lightly adapted using LoRA finetuning. We propose a two-part framework that includes: (1) text-conditioned generation, which uses a language instruction and the first frame, and (2) trajectory-conditioned generation, which uses a 2D trajectory overlay and the same initial frame. Experiments on the Jaco Play dataset, Bridge V2, and the RT1 dataset show that both modes produce smooth, coherent robot videos aligned with their respective conditions. Our findings indicate that pretrained image generators encode transferable temporal priors and can function as video-like robotic planners under minimal supervision. Code is released at \href{https://github.com/pangye202264690373/Image-Generation-as-a-Visual-Planner-for-Robotic-Manipulation}{https://github.com/pangye202264690373/Image-Generation-as-a-Visual-Planner-for-Robotic-Manipulation}.
Related papers
- BridgeV2W: Bridging Video Generation Models to Embodied World Models via Embodiment Masks [20.127101726681275]
Embodied world models have emerged as a promising paradigm in robotics.<n>We present BridgeV2W, which converts coordinate-space actions into pixel-aligned embodiment masks.<n>These masks are injected into a pretrained video generation model via a ControlNet-style pathway.
arXiv Detail & Related papers (2026-02-03T17:56:28Z) - DRAW2ACT: Turning Depth-Encoded Trajectories into Robotic Demonstration Videos [24.681248200255975]
Video models provide powerful real-world simulators for embodied AI but remain limited in controllability for robotic manipulation.<n>We present DRAW2ACT, a trajectory-conditioned video generation framework that extracts multiple representations from the input trajectory.<n>We show that DRAW2ACT achieves superior visual fidelity and consistency while yielding higher manipulation success rates compared to existing baselines.
arXiv Detail & Related papers (2025-12-16T09:11:36Z) - Lyra: Generative 3D Scene Reconstruction via Video Diffusion Model Self-Distillation [87.91642226587294]
Current learning-based 3D reconstruction methods rely on the availability of captured real-world multi-view data.<n>We propose a self-distillation framework that distills the implicit 3D knowledge in the video diffusion models into an explicit 3D Gaussian Splatting (3DGS) representation.<n>Our framework achieves state-of-the-art performance in static and dynamic 3D scene generation.
arXiv Detail & Related papers (2025-09-23T17:58:01Z) - cVLA: Towards Efficient Camera-Space VLAs [26.781510474119845]
Vision-Language-Action (VLA) models offer a compelling framework for tackling complex robotic manipulation tasks.<n>We propose a novel VLA approach that leverages the competitive performance of Vision Language Models on 2D images.<n>Our model predicts trajectory waypoints, making it both more efficient to train and robot embodiment.
arXiv Detail & Related papers (2025-07-02T22:56:41Z) - Geometry-aware 4D Video Generation for Robot Manipulation [28.709339959536106]
We propose a 4D video generation model that enforces multi-view 3D consistency of videos by supervising the model with cross-view pointmap alignment during training.<n>This geometric supervision enables the model to learn a shared 3D representation of the scene, allowing it to predict future video sequences from novel viewpoints.<n>Compared to existing baselines, our method produces more visually stable and spatially aligned predictions across multiple simulated and real-world robotic datasets.
arXiv Detail & Related papers (2025-07-01T18:01:41Z) - RoboEnvision: A Long-Horizon Video Generation Model for Multi-Task Robot Manipulation [30.252593687028767]
We address the problem of generating long-horizon videos for robotic manipulation tasks.<n>We propose a novel pipeline that bypasses the need for autoregressive generation.<n>Our approach achieves state-of-the-art results on two benchmarks in video quality and consistency.
arXiv Detail & Related papers (2025-06-27T08:21:55Z) - Dimension-Reduction Attack! Video Generative Models are Experts on Controllable Image Synthesis [12.160537328404622]
textttDRA-Ctrl provides new insights into reusing resource-intensive video models.<n>textttDRA-Ctrl lays foundation for future unified generative models across visual modalities.
arXiv Detail & Related papers (2025-05-29T10:34:45Z) - DreamGen: Unlocking Generalization in Robot Learning through Video World Models [120.25799361925387]
DreamGen is a pipeline for training robot policies that generalize across behaviors and environments through neural trajectories.<n>Our work establishes a promising new axis for scaling robot learning well beyond manual data collection.
arXiv Detail & Related papers (2025-05-19T04:55:39Z) - Follow Your Pose: Pose-Guided Text-to-Video Generation using Pose-Free
Videos [107.65147103102662]
In this work, we utilize datasets (i.e.,image pose pair and pose-free video) and the pre-trained text-to-image (T2I) model to obtain the pose-controllable character videos.
Specifically, in the first stage, only the keypoint-image pairs are used only for a controllable text-to-image generation.
In the second stage, we finetune the motion of the above network via a pose-free video dataset by adding the learnable temporal self-attention and reformed cross-frame self-attention blocks.
arXiv Detail & Related papers (2023-04-03T17:55:14Z) - Seer: Language Instructed Video Prediction with Latent Diffusion Models [43.708550061909754]
Text-conditioned video prediction (TVP) is an essential task to facilitate general robot policy learning.
We propose a sample and computation-efficient model, named textbfSeer, by inflating the pretrained text-to-image (T2I) stable diffusion models along the temporal axis.
With the adaptable-designed architecture, Seer makes it possible to generate high-fidelity, coherent, and instruction-aligned video frames.
arXiv Detail & Related papers (2023-03-27T03:12:24Z) - Learning Universal Policies via Text-Guided Video Generation [179.6347119101618]
A goal of artificial intelligence is to construct an agent that can solve a wide variety of tasks.
Recent progress in text-guided image synthesis has yielded models with an impressive ability to generate complex novel images.
We investigate whether such tools can be used to construct more general-purpose agents.
arXiv Detail & Related papers (2023-01-31T21:28:13Z) - Make-A-Video: Text-to-Video Generation without Text-Video Data [69.20996352229422]
Make-A-Video is an approach for translating the tremendous recent progress in Text-to-Image (T2I) generation to Text-to-Video (T2V)
We design a simple yet effective way to build on T2I models with novel and effective spatial-temporal modules.
In all aspects, spatial and temporal resolution, faithfulness to text, and quality, Make-A-Video sets the new state-of-the-art in text-to-video generation.
arXiv Detail & Related papers (2022-09-29T13:59:46Z) - Future Frame Prediction for Robot-assisted Surgery [57.18185972461453]
We propose a ternary prior guided variational autoencoder (TPG-VAE) model for future frame prediction in robotic surgical video sequences.
Besides content distribution, our model learns motion distribution, which is novel to handle the small movements of surgical tools.
arXiv Detail & Related papers (2021-03-18T15:12:06Z)
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.