通过关系差异特征提取，实现零样本物体导航的直接仿真到现实迁移。
Difference-Based Relational Learning for Zero-Shot Object-Goal Visual Navigation With Direct Sim-to-Real Transfer
- 利用时序差分与关系对比，生成跨域不变的物体特征表示。
- 在AI2-THOR上成功率显著优于基线，在真实机器人上验证有效。
- 适合关注仿真到现实迁移、视觉导航任务的研究者。
端到端深度强化学习在零样本物体目标视觉导航中仍受仿真到现实差距的挑战，尤其体现在物体外观变化和受限视野下。本文提出时间差分-关系网络（T-DRN），通过双帧时序缓冲保持窄视野下的短期物体连续性，并结合孪生差分特征提取器，计算目标与观测物体间的关系差异，生成域无关表征。大量实验表明，T-DRN在AI2-THOR环境中的零样本泛化能力显著优于强基线。此外，该方法在真实轮式机器人上系统性验证，展示了在实际感知与执行约束下的鲁棒性能，支持了直接仿真到现实迁移的可行性。
原文摘要 · Abstract (English)
End-to-end deep reinforcement learning (DRL) for zero-shot object-goal visual navigation remains challenged by the sim-to-real gap, particularly variations in object appearance and restricted camera field-of-view (FoV). This letter proposes a Temporal Difference-Relational Network (T-DRN) for robust zero-shot sim-to-real transfer. T-DRN combines a Siamese difference-based feature extractor, which computes relational difference between the target and observed objects to produce domain-independent representations, with a dual-frame temporal buffer that preserves short-term object continuity under narrow FoV. Extensive experiments in AI2-THOR demonstrate that T-DRN improves zero-shot generalization in terms of success rates over strong baselines. Furthermore, T-DRN is systematically validated on a physical wheeled robot, demonstrating robust performance under real sensing and actuation constraints and supporting the feasibility of direct sim-to-real transfer.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。