Peking University and Beijing Institute for Foundations of AI Team Sets New Record with Humanoid Robot Playing Table Tennis Over 1,000 Rallies
破纪录！人形机器人与人自主连续对打乒乓球超1000拍
On November 5, a joint team from Peking University and the Beijing Institute for Foundations of AI announced that the Zhiyuan Expy A3 humanoid robot platform successfully completed over 1,000 consecutive table tennis rallies against a human opponent. The fully autonomous interaction, involving ball perception, trajectory prediction, whole-body motion planning, and stroke execution without remote control or scripts, set a new record for high-speed dynamic human-robot confrontation. This achievement marks a transition from short-term performance demonstrations to long-term stable dynamic interaction, providing technical validation for deploying humanoid robots in complex dynamic operation scenarios.
Source: Chinese robotics — Hardware and sensing · Read original article ↗
Article text · Machine translation into English
On November 5, a joint team from Peking University and Beijing Academy of Artificial Intelligence (BAAI) announced that, relying on the AGIBOT AGIBOT A3 humanoid robot platform, they successfully completed a continuous ping-pong match between a humanoid robot and a human, breaking the 1000-stroke continuous round mark and once again refreshing the new record for high-speed dynamic human-robot confrontation for humanoid robots.
This confrontation was fully autonomous and closed-loop throughout, with no remote control and no preset scripts; the robot independently completed incoming ball perception, trajectory prediction, full-body motion planning, and stroke execution, continuously responding to the variable spin, landing points, and rhythm of the human player's incoming balls, verifying the perception, decision-making, and full-body collaborative control capabilities of embodied intelligence in high-speed dynamic interaction scenarios.
Ping-pong is regarded as one of the most difficult dynamic interaction scenarios for humanoid robots, with fast ball speeds and variable spins, leaving only a millisecond-level reaction window for the robot, posing extremely high challenges to algorithms and physical control. Previously, the Peking University-BAAI joint team had used the AGIBOT A3 platform to win the ping-pong tournament at the HOPE World Humanoid Robot Sports Meet, demonstrating the powerful potential of this industry-university-research joint solution.
Industry insiders stated that this breakthrough of 1000 continuous strokes marks the transition of humanoid robots from short-term performance-style confrontation toward long-term stable and continuous dynamic interaction, further shortening the distance from the laboratory to real interaction scenarios, providing important technical verification for the deployment of humanoid robots in more dynamic work scenarios.
(Original news work by Beijing Daily News Agency)
Source: Beijing Daily Client
If there are issues regarding work content, copyright, etc., please contact this website within 30 days from the date of publication of the relevant article. Copyright infringement contact number: 010-85201664
Scan the QR code
Download mobile client
Share to
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
trajectories
1,000
Trajectory count
View original evidence
11月5日，北京大学与北京智源人工智能研究院联合团队宣布，依托智元远征A3人形机器人平台，成功完成人形机器人与人类连续乒乓球对打，连续回合突破1000拍，再次刷新人形机器人高速动态人机对抗新纪录。
Open source S2trajectories
1,000
Trajectory count
- paper: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
11月5日，北京大学与北京智源人工智能研究院联合团队宣布，依托智元远征A3人形机器人平台，成功完成人形机器人与人类连续乒乓球对打，连续回合突破1000拍，再次刷新人形机器人高速动态人机对抗新纪录。
Open source S2
本次对抗全程自主闭环，无遥控、无预设脚本，机器人独立完成来球感知、轨迹预测、全身运动规划与击球执行，持续应对人类选手多变的来球旋转、落点与节奏，验证了具身智能在高速动态交互场景下的感知、决策与全身协同控制能力。
Open source S3
破纪录！人形机器人与人自主连续对打乒乓球超1000拍
Open source S1
业内人士表示，此次连续1000拍的突破，标志着人形机器人从短期表演式对打，迈向长时间稳定持续动态交互，进一步缩短从实验室走向真实交互场景的距离，为人形机器人在更多动态作业场景落地提供重要技术验证。
Open source S6
Source：Chinese robotics — Hardware and sensing · news.bjd.com.cn