2026-03-16.md 5.8 KB
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 | arXiv | PDF
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 | arXiv | PDF
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 | arXiv | PDF
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 | arXiv | PDF
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 | arXiv | PDF
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 | arXiv | PDF
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 | arXiv | PDF