Apple Research Team Discloses Progress on Humanoid Robot 'Brain' System
苹果研究团队披露机器人“大脑”进展 单项任务测试平均成功率为91%
Apple Research disclosed progress on its humanoid robot autonomous control system, HALO, via an arXiv preprint titled "Perceptive Humanoid Motion from Simulation." The team released code for data generation, model training, and deployment covering action generation, tracking, task orchestration, simulation, and real-robot execution. Tests in six unscanned real-world environments achieved a 91% average success rate across 360 single tasks; combined task tests in one environment averaged 70%. The study acknowledges that simulation data still requires human prompts and motion capture for new physical tasks, and notes limitations in visual-only perception for contact-heavy or occluded-object scenarios.
Source: Chinese robotics — Research news discovery · Read original article ↗
Article text · Machine translation into English
Apple research team discloses progress on robot “brain” Average success rate for single-task tests is 91%
Cailian Press, November 7: The Apple Research team disclosed research progress on the robot “brain.” On November 4 local time, the Apple Research team submitted the paper “Perceptive Humanoid Motion from Simulation” to the preprint platform arXiv, disclosing the humanoid robot autonomous control system HALO. The research team also released data generation, model training, and deployment code, covering aspects such as motion generation, motion tracking, task orchestration, as well as simulation and real robot operation. According to the paper, the research team conducted 360 single-task tests in 6 real environments that were not pre-scanned or mapped, with an average success rate of 91%. Additionally, in one real environment, 10 tests were conducted for 3 types of combined tasks, with an average success rate of 70%. Among them, the combination of “bend down to pass through an obstacle—walk toward the target—push a box” had a success rate of 50%. It should be noted that the simulation-generated data was not completely free from human participation. The team stated that new physical tasks still require manually written prompts and designed motion keyframes, and some motions also used human videos or motion capture materials. The research team also pointed out that currently, the robot mainly relies on visual perception and finds it difficult to directly perceive contact situations; when objects leave the camera's field of view or are obstructed, the performance of tasks involving many contacts will decline. Adding tactile perception is one of the subsequent directions for improvement. (The Paper News)
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%
Unique elapsed hours
test_count
360
Reported trials
Reported success rate
70%
Reported trials
test_count
10
Reported trials
Reported success rate
50%
Reported trials
environment_count
6
task_combination_count
3
Reported trials
- paper: not_reported
- code: Reported pending Open source S4
- weights: not_reported
- dataset: 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.
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 S5
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
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 S6
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 S7
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 S4
Source:Chinese robotics — Research news discovery · cls.cn