feat(robotdaily): 生成 2026-03-16 日报并准备推送

site/content/ai-daily/2026-03-16.md

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+---
+title: "2026-03-16 · AI 每日简报"
+date: 2026-03-16T16:15:32.720890+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
+> 应用信号: real-world, deployment, robot；创新信号: diffusion, zero-shot；领域匹配: robot, humanoid, physical
+- 作者：Yangsong Zhang, Anujith Muraleedharan, Rikhat Akizhanov, Abdul Ahad Butt 等另外4人
+- 标签：—
+- 中文摘要：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 for character animation and real robot control by applying a Whole-Body Controller (WBC) that converts diffusion-generated motions into executable trajectories. While WBC trajectories become compliant with physics, they may expos…
+- 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
+> 应用信号: real-world, deployment, robot；创新信号: first；领域匹配: robot, robotics, manipulation
+- 作者：Amber Xie, Haozhi Qi, Dorsa Sadigh
+- 标签：—
+- 中文摘要：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-precision tasks. While reinforcement learning and simulation-to-real-world transfer offer a promising alternative, the transferred policies often fail for tasks demanding millimeter-scale precision, such as bimanual piano playing.…
+- 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
+> 应用信号: system；创新信号: self-supervised；领域匹配: representation, representations, latent
+- 作者：Helen Qu, Rudy Morel, Michael McCabe, Alberto Bietti 等另外3人
+- 标签：—
+- 中文摘要：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 are computationally expensive to train and are subject to performance pitfalls, such as compounding errors during autoregressive rollout. In this work, we take a different perspective and look at scientific tasks further downstre…
+- 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
+> 应用信号: system, navigation, autonomous；创新信号: unified；领域匹配: representation, representations, embedding
+- 作者：Reza T. Batley, Apurba Sarker, Rajib Mostakim, Andrew Klichine 等另外1人
+- 标签：—
+- 中文摘要：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 separable neural architecture (SNA) addresses this by formalising a representational class that unifies additive, quadratic and tensor-decomposed neural models. By constraining interaction order and tensor rank, SNAs impose a structural i…
+- 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
+> 应用信号: robot, robotic, dataset；创新信号: novel, first；领域匹配: representation, representations, feature
+- 作者：Juhye Park, Wooju Lee, Dasol Hong, Changki Sung 等另外3人
+- 标签：—
+- 中文摘要：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 predicts the 3-DoF camera pose corresponding to a ground-view image with respect to a geo-referenced satellite image. However, existing methods struggle to bridge the significant viewpoint gap between the ground and satellite views mainl…
+- 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
+> 应用信号: real-world, deployment, robot；创新信号: zero-shot；领域匹配: reinforcement learning, policy optimization, control
+- 作者：Raphael Trumpp, Denis Hoornaert, Mirco Theile, Marco Caccamo
+- 标签：—
+- 中文摘要：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 particularly evident in autonomous racing, a domain that serves as a challenging benchmark for real-world DRL. However, deploying RPL-based controllers introduces system complexity and increases inference latency. We address this by i…
+- 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
+> 应用信号: deployed, robot, dataset；创新信号: diffusion, zero-shot；领域匹配: reinforcement learning, policy optimization, imitation learning
+- 作者：Junhe Sheng, Ruofei Bai, Kuan Xu, Ruimeng Liu 等另外4人
+- 标签：—
+- 中文摘要：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-mapping-planning pipeline and achieving strong zero-shot generalization. However, their performance remains constrained by the coverage of offline datasets, and when deployed in unseen settings, distribution shift often leads to ac…
+- 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)

test_send_discord.py

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+#!/usr/bin/env python3
+"""发送 Discord 消息到 RobotDaily 频道."""
+
+import requests
+import os
+
+def send_discord_message(channel_id: str, message: str) -> bool:
+    """通过 OpenClaw 的 Discord 功能发送消息."""
+    
+    # 读取配置文件中的 Discord 令牌
+    env_file = "/home/zhn/.openclaw/workspace/skills/robdaily/arxiv-digest/.env"
+    
+    if not os.path.exists(env_file):
+        print(f"❌ 配置文件不存在：{env_file}")
+        return False
+    
+    discord_token = None
+    with open(env_file, 'r') as f:
+        for line in f:
+            if line.startswith('DISCORD_BOT_TOKEN='):
+                discord_token = line.split('=', 1)[1].strip()
+                break
+    
+    if not discord_token or discord_token == "your_bot_token_here":
+        print("❌ 未配置 Discord 令牌，请编辑 .env 文件")
+        return False
+    
+    url = f"https://discord.com/api/v10/channels/{channel_id}/messages"
+    headers = {
+        "Authorization": f"Bot {discord_token}",
+        "Content-Type": "application/json",
+    }
+    
+    data = {"content": message}
+    
+    try:
+        response = requests.post(url, json=data, headers=headers, timeout=10)
+        response.raise_for_status()
+        print(f"✅ 消息已发送到 Discord 频道 {channel_id}")
+        return True
+    except requests.exceptions.RequestException as e:
+        print(f"❌ Discord 消息发送失败：{e}")
+        return False
+
+
+if __name__ == "__main__":
+    channel_id = "1481632217930141697"
+    
+    message = """📰 RobotDaily 2026-03-16
+
+今日精选 7 篇论文：
+• 具身智能：2 篇
+• 表征学习：3 篇  
+• 强化学习：2 篇
+
+🔗 查看完整简报：
+https://indigofloyd.space/ai-daily/2026-03-16/
+
+祝有充实的一天！✨"""
+    
+    send_discord_message(channel_id, message)