Apple Research Team Discloses Humanoid Robot 'Brain' HALO at 2027 Zhengzhou Robotics Exhibition
2027郑州机器人展：平均成功率91％！苹果研究团队披露机器人“大脑”进展
On November 4, Apple's research team submitted a paper to arXiv detailing HALO, an autonomous control system for humanoid robots validated on Unitree G1 units. The system operates in three layers: a visual language model for task planning, an action generation module, and a reinforcement learning-based tracker. Tests across six unscanned environments showed an average success rate of 91% over 360 single-task trials. The team also released code for data generation, training, and deployment.
Source: Chinese robotics — Research news discovery · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
- Control
- Reported autonomousOpen source S3
Reported numbers
Reported success rate
91%
Reported trials
View original evidence
图片来源：HALO项目页面截图拼接 据论文披露，研究团队在6个未预先扫描或建图的真实环境中开展360次单项任务测试，平均成功率为91%。
Open source S14Reported success rate
70%
Reported trials
Reported success rate
50%
Reported trials
trial_count
360
Reported trials
View original evidence
图片来源：HALO项目页面截图拼接 据论文披露，研究团队在6个未预先扫描或建图的真实环境中开展360次单项任务测试，平均成功率为91%。
Open source S14trial_count
10
Reported trials
training_hours
≈300 hours
View original evidence
研究团队在1000个程序生成的模拟家庭环境中，合成了约300小时的主要训练数据，将机器人“看到什么”“接到什么指令”和“如何运动”配对起来。
Open source S18gpu_count
250
synthesis_hours
≈48 hours
- paper: not_reported
- code: Reported pending Open source S5
- dataset: not_reported
- weights: not_reported
- code: not_reported
What remains unknown
Not established in the collected evidence: Environment, Data origin.
Reported performance applies to the described task. It does not establish general autonomy or deployment readiness.
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
当地时间11月4日，苹果研究团队在预印本平台arXiv提交论文《Perceptive Humanoid Motion from Simulation》，披露了人形机器人自主控制系统HALO。
Open source S3
HALO的合成数据生成与训练流程：先生成动作轨迹，将其放入虚拟场景，再渲染机器人视角的画面，形成配套训练数据，用于训练动作生成器和动作跟踪器。
Open source S22
图片来源：HALO项目页面截图拼接 据论文披露，研究团队在6个未预先扫描或建图的真实环境中开展360次单项任务测试，平均成功率为91%。
Open source S14
另在一个真实环境中，对3种组合任务分别开展10次测试，平均成功率为70%。
Open source S15
其中，“俯身通过障碍—走向目标—推箱子”这一组合的成功率为50%。
Open source S16
研究团队在1000个程序生成的模拟家庭环境中，合成了约300小时的主要训练数据，将机器人“看到什么”“接到什么指令”和“如何运动”配对起来。
Open source S18
图片来源：HALO论文 据介绍，研究团队使用250张英伟达H100 GPU，耗时约48小时合成主要训练数据。
Open source S23
研究团队还公开了数据生成、模型训练和部署代码，涵盖动作生成、动作跟踪、任务编排以及仿真和真实机器人运行等环节。
Open source S5
Source：Chinese robotics — Research news discovery · sohu.com