Multimodal embodiment-aware navigation transformer

• URL: http://arxiv.org/abs/2604.19267v1
• Date: Tue, 21 Apr 2026 09:32:59 GMT
• Title: Multimodal embodiment-aware navigation transformer
• Abstract summary: ViLiNT is a multimodal, attention-based policy for goal navigation, trained on heterogeneous data from multiple platforms and environments.<n>We show that ViLiNT improves Success Rate on average by 166% over equivalent state-of-the-art vision-only baseline (NoMaD)<n>Results are confirmed through real-world deployments of a rover navigating in obstacle fields.
• Score: 5.673561896367136
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Goal-conditioned navigation models for ground robots trained using supervised learning show promising zero-shot transfer, but their collision-avoidance capability nevertheless degrades under distribution shift, i.e. environmental, robot or sensor configuration changes. We propose ViLiNT a multimodal, attention-based policy for goal navigation, trained on heterogeneous data from multiple platforms and environments, which improves robustness with two key features. First, we fuse RGB images, 3D LiDAR point clouds, a goal embedding and a robot's embodiment descriptor with a transformer architecture to capture complementary geometry and appearance cues. The transformer's output is used to condition a diffusion model that generates navigable trajectories. Second, using automatically generated offline labels, we train a path clearance prediction head for scoring and ranking trajectories produced by the diffusion model. The diffusion conditioning as well as the trajectory ranking head depend on a robot's embodiment token that allows our model to generate and select trajectories with respect to the robot's dimensions. Across three simulated environments, ViLiNT improves Success Rate on average by 166\% over equivalent state-of-the-art vision-only baseline (NoMaD). This increase in performance is confirmed through real-world deployments of a rover navigating in obstacle fields. These results highlight that combining multimodal fusion with our collision prediction mechanism leads to improved off-road navigation robustness.

Related papers

• Qwen-VLA: Unifying Vision-Language-Action Modeling across Tasks, Environments, and Robot Embodiments [96.23886784364997]
  Embodied intelligence is often studied through specialized models for individual tasks such as manipulation or navigation.<n>We present Qwen-VLA, a unified embodied foundation model that extends Qwen's vision-language modeling stack to continuous action and trajectory generation.
  arXiv  Detail & Related papers  (2026-05-28T17:36:31Z)
• Can Vision Foundation Models Navigate? Zero-Shot Real-World Evaluation and Lessons Learned [5.561294055181353]
  Visual Navigation Models (VNMs) promise generalizable, robot navigation by learning from large-scale visual demonstrations.<n>We present a real-world evaluation of five state-of-the-art VNMs across two robot platforms and five environments spanning indoor and outdoor settings.
  arXiv  Detail & Related papers  (2026-03-26T22:04:49Z)
• Robometer: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisons [69.87766750714945]
  General-purpose robot reward models are typically trained to predict absolute task progress from expert demonstrations.<n>We introduce Robometer, a scalable reward modeling framework that combines intra-trajectory progress supervision with inter-trajectory preference supervision.<n>Robometer is trained with a dual objective: a frame-level progress loss that anchors reward magnitude on expert data, and a trajectory-comparison preference loss that imposes global ordering constraints.
  arXiv  Detail & Related papers  (2026-03-02T17:38:58Z)
• 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)
• Dita: Scaling Diffusion Transformer for Generalist Vision-Language-Action Policy [73.75271615101754]
  We present Dita, a scalable framework that leverages Transformer architectures to directly denoise continuous action sequences.<n>Dita employs in-context conditioning -- enabling fine-grained alignment between denoised actions and raw visual tokens from historical observations.<n>Dita effectively integrates cross-embodiment datasets across diverse camera perspectives, observation scenes, tasks, and action spaces.
  arXiv  Detail & Related papers  (2025-03-25T15:19:56Z)
• Diffusion Transformer Policy [48.50988753948537]
  We propose a large multi-modal diffusion transformer, dubbed as Diffusion Transformer Policy, to model continuous end-effector actions.<n>By leveraging the scaling capability of transformers, the proposed approach can effectively model continuous end-effector actions across large diverse robot datasets.
  arXiv  Detail & Related papers  (2024-10-21T12:43:54Z)
• IRASim: A Fine-Grained World Model for Robot Manipulation [24.591694756757278]
  We present IRASim, a novel world model capable of generating videos with fine-grained robot-object interaction details.<n>We train a diffusion transformer and introduce a novel frame-level action-conditioning module within each transformer block to explicitly model and strengthen the action-frame alignment.
  arXiv  Detail & Related papers  (2024-06-20T17:50:16Z)
• NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration [57.15811390835294]
  This paper describes how we can train a single unified diffusion policy to handle both goal-directed navigation and goal-agnostic exploration. We show that this unified policy results in better overall performance when navigating to visually indicated goals in novel environments. Our experiments, conducted on a real-world mobile robot platform, show effective navigation in unseen environments in comparison with five alternative methods.
  arXiv  Detail & Related papers  (2023-10-11T21:07:14Z)
• SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots [112.2491765424719]
  We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal. We use model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints. recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution. A Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal.
  arXiv  Detail & Related papers  (2021-08-03T02:56:21Z)
• Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems [92.26462290867963]
  Kimera-Multi is the first multi-robot system that is robust and capable of identifying and rejecting incorrect inter and intra-robot loop closures. We demonstrate Kimera-Multi in photo-realistic simulations, SLAM benchmarking datasets, and challenging outdoor datasets collected using ground robots.
  arXiv  Detail & Related papers  (2021-06-28T03:56:40Z)

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.