A Cross-Embodiment Gripper Benchmark for Rigid-Object Manipulation in Aerial and Industrial Robotics
🤖 AI Summary
Existing YCB/NIST benchmarks evaluate grasping performance only on single-platform setups, lacking metrics for cross-platform adaptability and energy efficiency—critical for mobile and aerial robotics. To address this gap, we propose the Cross-Embodied Grasping Benchmark (CEGB), extending the YCB/NIST framework with three novel metrics: gripper transfer time, energy consumption per task, and task-oriented load capacity. CEGB systematically evaluates gripper reusability and energy-aware performance across heterogeneous robotic platforms during rigid-body manipulation. Experimental validation using a lightweight, self-locking gripper prototype yields an average reconfiguration time of 17.6 s, holding energy consumption of 1.5 J/10 s, operational cycle times of 3.2–3.9 s, and task success rates exceeding 90%. CEGB thus establishes the first standardized, energy-aware evaluation framework for cross-platform gripper transferability, filling a critical void in embodied grasping assessment for heterogeneous robot systems.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Existing benchmarks lack cross-embodiment transferability evaluation for robotic grippers.
Current metrics do not assess energy-aware performance in grasping and holding tasks.
There is no standardized method to evaluate gripper suitability across heterogeneous robotic systems.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Introduces a cross-embodiment gripper benchmark suite
Adds transfer-time, energy-consumption, and ideal payload metrics
Provides reproducible foundation for energy-aware gripper evaluation
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