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具身智能前沿追踪系统
by
Jessy-Huang
· GitHub ↗
· v1.0.0
· MIT-0
84
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Install in OpenClaw
/install embodied-ai-tracker
Description
具身智能领域前沿动态追踪与视频素材采集系统；覆盖顶会论文（ICRA/IROS/CoRL/CVPR/NeurIPS）、开源项目、实验室动态；优先采集有Demo视频的爆款工作；生成含发布时间/主页/代码/视频链接的结构化日报，支持视频号内容创作
README (SKILL.md)
具身智能前沿追踪者（增强版）
功能特性
核心能力
- 顶会论文追踪: ICRA/IROS/CoRL/CVPR/NeurIPS/ICLR等
- 开源项目评估: GitHub Stars增长、Issue活跃度分析
- 视频素材采集: YouTube/B站/项目主页Demo优先采集
- 实验室动态监控: Google DeepMind/Stanford/Berkeley等顶级机构
- 学者动态追踪: Sergey Levine/Chelsea Finn/Jim Fan等领军人物
输出内容
每日报表包含：
- 【爆款素材】视频Demo优先区（★★★★★可直接剪辑）
- 重点论文（含arXiv链接/代码仓库/发布时间）
- 开源工程（Stars数/增长趋势/技术亮点）
- 实验室与产业动态
- 社区热议与趋势洞察
核心追踪范围
学术顶会与期刊
| 会议 | 领域 | 截稿时间 |
|---|---|---|
| ICRA | 机器人与自动化 | 9月 |
| IROS | 智能机器人系统 | 5月 |
| CoRL | 机器人学习 | 5月 |
| CVPR | 计算机视觉 | 9月 |
| NeurIPS | 人工智能 | 5月 |
| ICLR | 表征学习 | 9月 |
| RSS | 机器人科学 | 2月 |
| Humanoids | 人形机器人 | 7月 |
顶级实验室
美国: Stanford Robot Learning Lab, UC Berkeley BAIR, MIT CSAIL, CMU Robotics Institute, Google DeepMind, NVIDIA Research
中国: 上海交通大学智能机器人研究所, 清华大学智能技术与系统国家重点实验室, 宇树科技, 智元机器人, 傅利叶智能
欧洲: ETH Zurich, TU Munich, University of Tokyo
关键学者
Sergey Levine, Pieter Abbeel, Chelsea Finn, Jim Fan, Dorsa Sadigh, Fei-Fei Li, Shuran Song, Karol Hausman
执行流程
第一阶段：多源并行搜索
1. 论文搜索
# 英文搜索 - arXiv新论文
site:arxiv.org embodied robot 2025
site:arxiv.org robot manipulation VLA 2025
site:arxiv.org humanoid robot 2025
site:arxiv.org imitation learning robot 2025
# 顶会
ICRA 2025 robot learning papers
CoRL 2025 robotics
CVPR 2025 robotic manipulation
# 中文
具身智能 机器人 最新论文 2025
2. GitHub项目搜索
embodied AI robot learning site:github.com stars:>500
robot manipulation foundation model site:github.com
humanoid robot control site:github.com
3. Demo视频搜索（重点！）
robot manipulation demo video 2025 site:youtube.com
humanoid robot impressive demo 2025
site:deepmind.google robot video
具身智能 机器人 demo 视频 site:bilibili.com
4. 动态追踪
# 学者Twitter/X
Sergey Levine robot new 2025
Chelsea Finn robot demo 2025
Jim Fan NVIDIA robotics 2025
# 机构动态
Stanford Robot Learning Lab publications 2025
Figure AI robot update 2025
Unitree robot 2025
第二阶段：信息筛选与分级
| 等级 | 标准 | 标注 |
|---|---|---|
| S级 | 有Demo视频 + 高影响力机构 + 新架构 | [爆款素材] |
| A级 | 有代码 + 技术突破 + 知名团队 | [A级论文] |
| B级 | 有代码 + 增量改进 | 有代码 |
| C级 | 纯论文无代码 | 理论工作 |
第三阶段：结构化输出
按以下模板生成日报：
# 具身智能前沿日报 | [YYYY-MM-DD]
## 今日核心热点
[关键词/主题]：[一句话总结]
## 【爆款素材】视频Demo优先区
### [工作名称] ★★★★★
- 发布时间: [YYYY-MM-DD]
- 发布机构: [机构名]
- 视频链接: [YouTube/B站URL]
- 论文: [arXiv URL]
- 代码: [GitHub URL]
- 素材评价: [画面质量/时长评估]
## 重点论文与新作
### [论文标题]
- 发布时间: [YYYY-MM-DD]
- 来源: [会议/ArXiv]
- 核心贡献: [100字内]
- 技术亮点: [关键技术细节]
- 相关链接:
- 论文: [URL]
- 代码: [URL]
- 视频: [URL]
## 开源与工程
### [仓库名称]
- Stars: [数字] | 月增长: [+XX]
- 功能: [描述]
- 链接: [GitHub URL]
## 实验室与产业动态
- [机构]: [动态内容] [时间] [链接]
## 趋势洞察
- [趋势1]: [分析]
- [趋势2]: [分析]
视频素材评级
| 评级 | 说明 | 用途 |
|---|---|---|
| ★★★★★ | 画面震撼、超长演示 | 直接用于视频开场 |
| ★★★★ | 有亮点片段、10秒+可用 | 剪辑核心素材 |
| ★★★ | 有片段可用 | 配合字幕和解说 |
输出质量要求
- 每条信息必须包含至少一个可访问的URL链接
- 论文必须标注arXiv ID或会议信息
- GitHub项目必须提供Stars数
- 必须搜索并标注有无Demo视频
- 论文优先选3个月内发布
- GitHub项目优先选6个月内有更新的
重点追踪方向
- 世界模型（World Models）
- 视觉-语言-动作模型（VLA）
- 跨具身迁移（Cross-Embodiment）
- 触觉感知（Tactile Sensing）
- 长时程操作（Long-Horizon Manipulation）
- 大模型驱动机器人（LLM-based Agents）
GitHub热门项目参考
- Open X-Embodiment: google-deepmind/open_x_embodiment (Stars: 10k+)
- RT-1/RT-2: google-research/robotics_transformer
- Mobile ALOHA: stanford-ali/mobile-aloha (Stars: 10k+)
- Genesis: Genesis-Embodied/Genesis (Stars: 10k+) - 新生代仿真引擎
- IsaacGym: NVIDIA/IsaacGymEnvs (Stars: 5k+)
- ACT: stanford-ali/ACT (Stars: 2k+) - 模仿学习
- RDT: thudm/robotics-diffusion-transformer - 清华出品
Usage Guidance
This skill is coherent with its stated goal of finding papers, GitHub repos, and demo videos and producing a daily markdown report. The main risk is the runtime tool set declared in the SKILL.md: exec_shell and read_file let the agent run arbitrary shell commands and read local files, which is more privilege than a pure web-scraper strictly needs. Before installing, consider: (1) require the skill to run in a sandboxed environment where exec_shell and file-read are limited or disabled; (2) ask the author to justify and narrow the allowed-tools (e.g., allow network fetch but not arbitrary file reads); (3) monitor what commands the agent runs and review logs; (4) do not provide any secrets (GitHub tokens, AWS keys) unless absolutely necessary and reviewed. If you cannot restrict shell/file access, treat this skill as higher-risk and prefer running it manually or in an isolated VM/container.
Capability Analysis
Type: OpenClaw Skill
Name: embodied-ai-tracker
Version: 1.0.0
The skill is a legitimate tool designed to track research papers, GitHub projects, and video demonstrations in the field of Embodied AI. The instructions in SKILL.md provide structured search queries and reporting templates aligned with its stated purpose, and although it requests broad shell access (exec_shell), there is no evidence of malicious intent, data exfiltration, or prompt injection attacks.
Capability Tags
Capability Assessment
Purpose & Capability
The name/description (embodied AI tracker + video collection) align with the SKILL.md instructions (site: searches, GitHub and video searches, structured report). The skill does not request unrelated environment variables or external services. However, the SKILL.md declares allowed-tools (Bash: grep_file, glob_file, exec_shell, read_file) that grant local shell and file access; while exec_shell can be used to run curl/wget for scraping (plausible), read_file/grep_file are not clearly necessary for the stated web-scraping and reporting purpose and therefore appear broader than required.
Instruction Scope
Instructions stay on-topic: they specify search queries, selection/filtering criteria, ranking tiers, and a markdown reporting template. They do not instruct the agent to collect unrelated system data or to transmit data to unexpected endpoints. The only scope concern is that the allowed-tools permit reading local files and arbitrary shell commands, but the SKILL.md content itself does not direct the agent to read user files or secrets.
Install Mechanism
No install spec and no code files — instruction-only. This minimizes supply-chain risk because nothing is downloaded or written to disk by an installer.
Credentials
The skill requests no environment variables, credentials, or config paths, which is proportionate for a public web-scraping/tracking tool. (Note: GitHub/API rate limits are not addressed — the skill appears to rely on public web pages/search without asking for API keys.)
Persistence & Privilege
always:false and no install means the skill does not demand persistent inclusion, which is good. However, allowed-tools (exec_shell, read_file) enable arbitrary shell execution and local file reads while the agent is invoked (and autonomous invocation is allowed by default). That combination increases risk if the agent is permitted to run unreviewed commands or operate without strict sandboxing.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install embodied-ai-tracker - After installation, invoke the skill by name or use
/embodied-ai-tracker - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
v1.0
Metadata
Frequently Asked Questions
What is 具身智能前沿追踪系统?
具身智能领域前沿动态追踪与视频素材采集系统；覆盖顶会论文（ICRA/IROS/CoRL/CVPR/NeurIPS）、开源项目、实验室动态；优先采集有Demo视频的爆款工作；生成含发布时间/主页/代码/视频链接的结构化日报，支持视频号内容创作. It is an AI Agent Skill for Claude Code / OpenClaw, with 84 downloads so far.
How do I install 具身智能前沿追踪系统?
Run "/install embodied-ai-tracker" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is 具身智能前沿追踪系统 free?
Yes, 具身智能前沿追踪系统 is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does 具身智能前沿追踪系统 support?
具身智能前沿追踪系统 is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created 具身智能前沿追踪系统?
It is built and maintained by Jessy-Huang (@jessy-huang); the current version is v1.0.0.
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