arXiv:2512.01598cs.RO2025-12

新基准评估空中与工业机器人抓取器跨平台性能和能耗,更贴近真实应用。

A Cross-Embodiment Gripper Benchmark for Rigid-Object Manipulation in Aerial and Industrial Robotics

  • 设计跨体态抓取基准,测试换体时间、能耗和任务适配性。
  • 换体平均仅需17.6秒,持握能耗低至每10秒1.5焦耳,成功率超90%。
  • 适合研究空中/工业机器人抓取器的可迁移性与能效优化者使用。

机器人抓取器正广泛部署于工业、协作及空中平台,不同机体形式带来独特的机械、能量和操作约束。现有YCB和NIST基准仅在单一平台评估抓取成功率、力或时序,未涵盖跨体态迁移性与能耗表现,而这正是现代移动与空中操作的关键需求。本文提出跨体态抓取器基准(CEGB),在YCB和部分NIST指标基础上扩展三项新组件:换体时间基准(衡量更换机体的实际耗时)、能耗基准(评估抓取与持握效率)、以及针对特定任务的理想负载评估(反映设计依赖的操作能力)。这些指标共同刻画抓取性能及单个抓取器在异构系统中的复用潜力。采用轻量自锁式抓取器原型作为参考案例,实验显示换体中位时间约17.6秒,原型持握能耗约1.5焦耳/10秒,抓取周期3.2–3.9秒,成功率超过90%。CEGB为航空与操纵领域提供了可复现的跨平台、能耗感知的抓取器评估基础。

原文摘要 · Abstract (English)

Robotic grippers are increasingly deployed across industrial, collaborative, and aerial platforms, where each embodiment imposes distinct mechanical, energetic, and operational constraints. Established YCB and NIST benchmarks quantify grasp success, force, or timing on a single platform, but do not evaluate cross-embodiment transferability or energy-aware performance, capabilities essential for modern mobile and aerial manipulation. This letter introduces the Cross-Embodiment Gripper Benchmark (CEGB), a compact and reproducible benchmarking suite extending YCB and selected NIST metrics with three additional components: a transfer-time benchmark measuring the practical effort required to exchange embodiments, an energy-consumption benchmark evaluating grasping and holding efficiency, and an intent-specific ideal payload assessment reflecting design-dependent operational capability. Together, these metrics characterize both grasp performance and the suitability of reusing a single gripper across heterogeneous robotic systems. A lightweight self-locking gripper prototype is implemented as a reference case. Experiments demonstrate rapid embodiment transfer (median ~= 17.6 s across user groups), low holding energy for gripper prototype (~= 1.5 J per 10 s), and consistent grasp performance with cycle times of 3.2 - 3.9 s and success rates exceeding 90%. CEGB thus provides a reproducible foundation for cross-platform, energy-aware evaluation of grippers in aerial and manipulators domains.

机器人抓取跨平台评估能耗优化

Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品;中文卡片由大模型生成,请以原文为准。