DreamToNav: Generalizable Navigation for Robots via Generative Video Planning

• URL: http://arxiv.org/abs/2603.06190v1
• Date: Fri, 06 Mar 2026 11:57:10 GMT
• Title: DreamToNav: Generalizable Navigation for Robots via Generative Video Planning
• Abstract summary: We present DreamToNav, a novel autonomous robot framework that uses generative video models to enable intuitive, human-in-the-loop control.<n>We evaluate the approach on both a wheeled mobile robot and a quadruped robot in indoor navigation tasks.<n>DreamToNav achieves a success rate of 76.7%, with final goal errors typically within 0.05-0.10 m and trajectory tracking errors below 0.15 m.
• Score: 1.964570633684439
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present DreamToNav, a novel autonomous robot framework that uses generative video models to enable intuitive, human-in-the-loop control. Instead of relying on rigid waypoint navigation, users provide natural language prompts (e.g. ``Follow the person carefully''), which the system translates into executable motion. Our pipeline first employs Qwen 2.5-VL-7B-Instruct to refine vague user instructions into precise visual descriptions. These descriptions condition NVIDIA Cosmos 2.5, a state-of-the-art video foundation model, to synthesize a physically consistent video sequence of the robot performing the task. From this synthetic video, we extract a valid kinematic path using visual pose estimation, robot detection and trajectory recovery. By treating video generation as a planning engine, DreamToNav allows robots to visually "dream" complex behaviors before executing them, providing a unified framework for obstacle avoidance and goal-directed navigation without task-specific engineering. We evaluate the approach on both a wheeled mobile robot and a quadruped robot in indoor navigation tasks. DreamToNav achieves a success rate of 76.7%, with final goal errors typically within 0.05-0.10 m and trajectory tracking errors below 0.15 m. These results demonstrate that trajectories extracted from generative video predictions can be reliably executed on physical robots across different locomotion platforms.

Related papers

• Uni-LaViRA: Language-Vision-Robot Actions Translation for Unified Embodied Navigation [60.07205156194741]
  Embodied navigation requires an agent to map language and visual observations to a stream of spatial actions that drive a real robot through environments it has never seen.<n>We present Uni-LaViRA, a unified agentic architecture that extends the same insight to four task families and to four heterogeneous real robots.
  arXiv  Detail & Related papers  (2026-05-26T18:52:04Z)
• ImagiNav: Scalable Embodied Navigation via Generative Visual Prediction and Inverse Dynamics [13.382453086651019]
  Vision-Language Navigation has relied on end-to-end policies trained on expensive, embodiment-specific robot data.<n>We propose ImagiNav, a novel modular paradigm that decouples visual planning from robot actuation.<n>ImagiNav demonstrates strong zero-shot transfer to robot navigation without requiring robot demonstrations.
  arXiv  Detail & Related papers  (2026-03-14T08:34:02Z)
• Learning to Localize Reference Trajectories in Image-Space for Visual Navigation [11.2075405205209]
  We present LoTIS, a model for visual navigation that provides robot-agnostic image-space guidance.<n>Instead of predicting actions tied to specific robots, we predict the image-space coordinates of the reference trajectory as they would appear in the robot's current view.<n>Our model's predictions provide guidance zero-shot across diverse embodiments.
  arXiv  Detail & Related papers  (2026-02-21T11:33:00Z)
• H2R-Grounder: A Paired-Data-Free Paradigm for Translating Human Interaction Videos into Physically Grounded Robot Videos [58.006918399913665]
  We propose a video-to-video translation framework that converts ordinary human-object interaction videos into motion-consistent robot manipulation videos.<n>Our approach does not require any paired human-robot videos for training only a set of unpaired robot videos, making the system easy to scale.<n>At test time, we apply the same process to human videos (inpainting the person and overlaying human pose cues) and generate high-quality robot videos that mimic the human's actions.
  arXiv  Detail & Related papers  (2025-12-10T07:59:45Z)
• VidBot: Learning Generalizable 3D Actions from In-the-Wild 2D Human Videos for Zero-Shot Robotic Manipulation [53.63540587160549]
  VidBot is a framework enabling zero-shot robotic manipulation using learned 3D affordance from in-the-wild monocular RGB-only human videos.<n> VidBot paves the way for leveraging everyday human videos to make robot learning more scalable.
  arXiv  Detail & Related papers  (2025-03-10T10:04:58Z)
• CANVAS: Commonsense-Aware Navigation System for Intuitive Human-Robot Interaction [19.997935470257794]
  We present CANVAS, a framework that combines visual and linguistic instructions for commonsense-aware navigation.<n>Its success is driven by imitation learning, enabling the robot to learn from human navigation behavior.<n>Our experiments show that CANVAS outperforms the strong rule-based system ROS NavStack across all environments.
  arXiv  Detail & Related papers  (2024-10-02T06:34:45Z)
• Track2Act: Predicting Point Tracks from Internet Videos enables Generalizable Robot Manipulation [65.46610405509338]
  We seek to learn a generalizable goal-conditioned policy that enables zero-shot robot manipulation. Our framework,Track2Act predicts tracks of how points in an image should move in future time-steps based on a goal. We show that this approach of combining scalably learned track prediction with a residual policy enables diverse generalizable robot manipulation.
  arXiv  Detail & Related papers  (2024-05-02T17:56:55Z)
• Vid2Robot: End-to-end Video-conditioned Policy Learning with Cross-Attention Transformers [36.497624484863785]
  We introduce Vid2Robot, an end-to-end video-conditioned policy that takes human videos demonstrating manipulation tasks as input and produces robot actions. Our model is trained with a large dataset of prompt video-robot trajectory pairs to learn unified representations of human and robot actions from videos. We evaluate Vid2Robot on real-world robots and observe over 20% improvement over BC-Z when using human prompt videos.
  arXiv  Detail & Related papers  (2024-03-19T17:47:37Z)
• Action-conditioned Deep Visual Prediction with RoAM, a new Indoor Human Motion Dataset for Autonomous Robots [1.7778609937758327]
  We introduce the Robot Autonomous Motion (RoAM) video dataset. It is collected with a custom-made turtlebot3 Burger robot in a variety of indoor environments recording various human motions from the robot's ego-vision. The dataset also includes synchronized records of the LiDAR scan and all control actions taken by the robot as it navigates around static and moving human agents.
  arXiv  Detail & Related papers  (2023-06-28T00:58:44Z)
• Robot Learning with Sensorimotor Pre-training [98.7755895548928]
  We present a self-supervised sensorimotor pre-training approach for robotics. Our model, called RPT, is a Transformer that operates on sequences of sensorimotor tokens. We find that sensorimotor pre-training consistently outperforms training from scratch, has favorable scaling properties, and enables transfer across different tasks, environments, and robots.
  arXiv  Detail & Related papers  (2023-06-16T17:58:10Z)
• Coupling Vision and Proprioception for Navigation of Legged Robots [65.59559699815512]
  We exploit the complementary strengths of vision and proprioception to achieve point goal navigation in a legged robot. We show superior performance compared to wheeled robot (LoCoBot) baselines. We also show the real-world deployment of our system on a quadruped robot with onboard sensors and compute.
  arXiv  Detail & Related papers  (2021-12-03T18:59:59Z)

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.