Abstract
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 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.
Blogger's Review: The introduction of GCA-Bench provides a comprehensive evaluation framework for robotic grasping tasks, particularly addressing the multi-step reasoning requirements in complex scenarios, which is of significant research value. The new evaluation metrics and failure analysis will facilitate further advancements in this field, and I look forward to more researchers delving deeper based on this.