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This paper introduces GCA-Bench, a novel benchmark designed to evaluate robotic grasping in complex scenarios that require multi-step reasoning and semantic understanding, addressing the limitations of existing benchmarks that focus solely on visual-based grasp pose detection. By implementing a variety of baseline methods, the authors demonstrate that current approaches achieve success rates below 70% in these challenging grasping tasks, highlighting significant shortcomings in the field. The study also proposes new evaluation metrics and analyzes failure modes to inform future advancements in robust grasping strategies.
Current robotic grasping methods struggle, with success rates under 70% in complex scenarios that demand reasoning and semantic understanding.
Robust robotic grasping remains a fundamental challenge for complex real-world applications. Recent advances in large-scale models demonstrate promising capabilities for reasoning in robotic tasks. However, existing benchmarks for grasping primarily focus on isolated, visual-based grasp pose detection, failing to capture the complexity of grasping tasks that require multi-step reasoning and semantic understanding during execution. To address this gap, we propose GCA-Bench, a benchmark featuring challenging \textit{grasping with complex action} scenarios that involve both scene-level reasoning and semantic constraints. GCA-Bench enables the evaluation of recent large foundation models under the same settings. To demonstrate the effectiveness of our new benchmark, we implement a diverse set of baselines, ranging from traditional grasp detection pipelines to end-to-end learning methods. Empirical studies achieve success rates below 70\% on complex grasping scenarios, underscoring critical limitations. In addition, we propose new evaluation metrics, analyze critical failure models, and provide insights to guide the development of more robust and generalizable grasping strategies.
