多模态具身感知导航Transformer
Multimodal embodiment-aware navigation transformer
AI总结：
本文提出ViLiNT，通过融合多模态数据提升机器人导航鲁棒性，利用Transformer和扩散模型生成可导航路径，实现在不同环境中导航成功率提升166%。
摘要：
针对地面机器人监督学习训练的导航模型在分布偏移下避障能力下降的问题，本文提出ViLiNT多模态注意力策略，通过融合RGB图像、3D点云、目标嵌入和机器人具身描述符，利用Transformer捕捉互补几何与外观线索，生成可导航轨迹。同时通过自动生成标签训练路径清除预测头，利用机器人具身令牌生成和选择路径。在三个模拟环境中，ViLiNT在等效视觉基线NoMaD上平均提升导航成功率166%，并通过实际部署验证其有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
