ManipulationNet: An Infrastructure for Benchmarking Real-World Robot Manipulation with Physical Skill Challenges and Embodied Multimodal Reasoning
2026-03-04 • Robotics
Robotics
AI summaryⓘ
The authors created ManipulationNet, a system to help robots practice and be tested on real-world tasks involving moving and handling objects. It provides standard hardware and software so many researchers can try the same tasks and compare results fairly. ManipulationNet includes two types of challenges: one focused on basic physical skills and another on more complex thinking and understanding. This setup aims to help scientists track progress in robot abilities and push toward robots that can manipulate things in everyday environments.
robotic manipulationphysical artificial intelligencebenchmark tasksstandardized hardwaredistributed evaluationphysical skillsembodied reasoningmultimodal grounding
Authors
Yiting Chen, Kenneth Kimble, Edward H. Adelson, Tamim Asfour, Podshara Chanrungmaneekul, Sachin Chitta, Yash Chitambar, Ziyang Chen, Ken Goldberg, Danica Kragic, Hui Li, Xiang Li, Yunzhu Li, Aaron Prather, Nancy Pollard, Maximo A. Roa-Garzon, Robert Seney, Shuo Sha, Shihefeng Wang, Yu Xiang, Kaifeng Zhang, Yuke Zhu, Kaiyu Hang
Abstract
Dexterous manipulation enables robots to purposefully alter the physical world, transforming them from passive observers into active agents in unstructured environments. This capability is the cornerstone of physical artificial intelligence. Despite decades of advances in hardware, perception, control, and learning, progress toward general manipulation systems remains fragmented due to the absence of widely adopted standard benchmarks. The central challenge lies in reconciling the variability of the real world with the reproducibility and authenticity required for rigorous scientific evaluation. To address this, we introduce ManipulationNet, a global infrastructure that hosts real-world benchmark tasks for robotic manipulation. ManipulationNet delivers reproducible task setups through standardized hardware kits, and enables distributed performance evaluation via a unified software client that delivers real-time task instructions and collects benchmarking results. As a persistent and scalable infrastructure, ManipulationNet organizes benchmark tasks into two complementary tracks: 1) the Physical Skills Track, which evaluates low-level physical interaction skills, and 2) the Embodied Reasoning Track, which tests high-level reasoning and multimodal grounding abilities. This design fosters the systematic growth of an interconnected network of real-world abilities and skills, paving the path toward general robotic manipulation. By enabling comparable manipulation research in the real world at scale, this infrastructure establishes a sustainable foundation for measuring long-term scientific progress and identifying capabilities ready for real-world deployment.