Benchmarking Real-World Robotic Manipulation through Stochastic Challenges

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为 VLA、具身智能、人形机器人控制、机器人操作等高价值论文补充中文说明。

Original abstract

A challenging bottleneck in manipulation research is reproducibility and comparability of methods in real-world environments. Since manipulation is an interactive task, it cannot be evaluated on a static dataset of observations. Benchmarking in simulation is a common remedy, but cannot cover the whole complexity of real-world tests. This paper proposes a new paradigm of stochastic challenges. Like the well-known YCB objects, it is based on reproducible real-world scenes and therefore allows system-level innovation and comparability between different platforms. Due to their stochastic nature, our challenges however, mimic the rigor and surprise of hidden test sets. To this end, we show how different robots in different labs can be compared based on their success rate over different random task samples, if these samples come from the same distribution. We propose stochastic challenges as a general paradigm for robot benchmarking and implement this paradigm in a novel benchmark for mobile manipulation, on which we compare two robots tested in two different labs.

Links and sources

• Official / arXiv page
• PDF from original source

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