--- title: "2026-03-16 · AI 每日简报" date: 2026-03-16T16:31:30.888993+08:00 draft: false summary: "RobotDaily 2026-03-16：共 7 篇，含 具身智能 2 篇，表征学习 3 篇，强化学习 2 篇。" tags: ["robotdaily", "ai-daily", "具身智能", "表征学习", "强化学习"] --- > Hugo 归档版，来源于 RobotDaily 当日 Markdown 简报。 > > RobotDaily 2026-03-16：共 7 篇，含 具身智能 2 篇，表征学习 3 篇，强化学习 2 篇。 偏应用导向精选，按方向整理成短卡片式 Markdown 归档。 ## 具身智能（2 篇） ### 1. PhysMoDPO: Physically-Plausible Humanoid Motion with Preference Optimization > PhysMoDPO，采用扩散模型解决机器人操作，实现真实部署 - 作者：Yangsong Zhang, Anujith Muraleedharan, Rikhat Akizhanov, Abdul Ahad Butt 等另外4人 - 标签：`具身智能` `机器人` `真实部署` `操控` - 中文摘要：【LLM 暂不可用，先保留英文摘要要点】Recent progress in text-conditioned human motion generation has been largely driven by diffusion models trained on large-scale human motion data. Building on this progress, recent methods attempt to transfer such models… - 链接：[DOI](https://doi.org/10.48550/arXiv.2603.13228) | [arXiv](https://arxiv.org/abs/2603.13228v1) | [PDF](https://arxiv.org/pdf/2603.13228v1) ### 2. HandelBot: Real-World Piano Playing via Fast Adaptation of Dexterous Robot Policies > HandelBot，采用强化学习解决机器人操作，实现真实部署 - 作者：Amber Xie, Haozhi Qi, Dorsa Sadigh - 标签：`具身智能` `机器人` `真实部署` `操控` - 中文摘要：【LLM 暂不可用，先保留英文摘要要点】Mastering dexterous manipulation with multi-fingered hands has been a grand challenge in robotics for decades. Despite its potential, the difficulty of collecting high-quality data remains a primary bottleneck for high-… - 链接：[DOI](https://doi.org/10.48550/arXiv.2603.12243) | [arXiv](https://arxiv.org/abs/2603.12243v1) | [PDF](https://arxiv.org/pdf/2603.12243v1) ## 表征学习（3 篇） ### 1. Representation Learning for Spatiotemporal Physical Systems > Representation Learning for Spatiotemporal Physical Systems，采用自监督学习解决相关任务，实现性能优化 - 作者：Helen Qu, Rudy Morel, Michael McCabe, Alberto Bietti 等另外3人 - 标签：`表征学习` `潜在空间` `世界模型` `预训练` - 中文摘要：【LLM 暂不可用，先保留英文摘要要点】Machine learning approaches to spatiotemporal physical systems have primarily focused on next-frame prediction, with the goal of learning an accurate emulator for the system's evolution in time. However, these emulators… - 链接：[DOI](https://doi.org/10.48550/arXiv.2603.13227) | [arXiv](https://arxiv.org/abs/2603.13227v1) | [PDF](https://arxiv.org/pdf/2603.13227v1) ### 2. Separable neural architectures as a primitive for unified predictive and generative intelligence > Separable neural architectures as a primitive for unified predictive and generative i… - 作者：Reza T. Batley, Apurba Sarker, Rajib Mostakim, Andrew Klichine 等另外1人 - 标签：`表征学习` `潜在空间` `世界模型` `预训练` - 中文摘要：【LLM 暂不可用，先保留英文摘要要点】Intelligent systems across physics, language and perception often exhibit factorisable structure, yet are typically modelled by monolithic neural architectures that do not explicitly exploit this structure. The separabl… - 链接：[DOI](https://doi.org/10.48550/arXiv.2603.12244) | [arXiv](https://arxiv.org/abs/2603.12244v1) | [PDF](https://arxiv.org/pdf/2603.12244v1) ### 3. VIRD: View-Invariant Representation through Dual-Axis Transformation for Cross-View Pose Estimation > VIRD，采用表征学习解决机器人操作，实现首次提出 - 作者：Juhye Park, Wooju Lee, Dasol Hong, Changki Sung 等另外3人 - 标签：`表征学习` `潜在空间` `世界模型` `预训练` - 中文摘要：【LLM 暂不可用，先保留英文摘要要点】Accurate global localization is crucial for autonomous driving and robotics, but GNSS-based approaches often degrade due to occlusion and multipath effects. As an emerging alternative, cross-view pose estimation predict… - 链接：[DOI](https://doi.org/10.48550/arXiv.2603.12918) | [arXiv](https://arxiv.org/abs/2603.12918v1) | [PDF](https://arxiv.org/pdf/2603.12918v1) ## 强化学习（2 篇） ### 1. Efficient Real-World Autonomous Racing via Attenuated Residual Policy Optimization > Efficient Real-World Autonomous Racing via Attenuated Residual Policy Optimization，采用… - 作者：Raphael Trumpp, Denis Hoornaert, Mirco Theile, Marco Caccamo - 标签：`强化学习` `策略优化` `奖励设计` `离线 RL` - 中文摘要：【LLM 暂不可用，先保留英文摘要要点】Residual policy learning (RPL), in which a learned policy refines a static base policy using deep reinforcement learning (DRL), has shown strong performance across various robotic applications. Its effectiveness is part… - 链接：[DOI](https://doi.org/10.48550/arXiv.2603.12960) | [arXiv](https://arxiv.org/abs/2603.12960v1) | [PDF](https://arxiv.org/pdf/2603.12960v1) ### 2. Beyond Imitation: Reinforcement Learning Fine-Tuning for Adaptive Diffusion Navigation Policies > Beyond Imitation，采用扩散模型解决机器人操作，实现零样本泛化 - 作者：Junhe Sheng, Ruofei Bai, Kuan Xu, Ruimeng Liu 等另外4人 - 标签：`强化学习` `策略优化` `奖励设计` `离线 RL` - 中文摘要：【LLM 暂不可用，先保留英文摘要要点】Diffusion-based robot navigation policies trained on large-scale imitation learning datasets, can generate multi-modal trajectories directly from the robot's visual observations, bypassing the traditional localization-m… - 链接：[DOI](https://doi.org/10.48550/arXiv.2603.12868) | [arXiv](https://arxiv.org/abs/2603.12868v1) | [PDF](https://arxiv.org/pdf/2603.12868v1)