Generalizing language-conditioned robotic policies to new tasks remains a significant challenge, hampered by the lack of suitable simulation benchmarks. In this paper, we address this gap by introducing GemBench, a novel benchmark to assess generalization capabilities of vision-language robotic manipulation policies. GemBench incorporates seven general action primitives and four levels of generalization, spanning novel placements, rigid and articulated objects, and complex long-horizon tasks. We evaluate state-of-the-art approaches on GemBench and also introduce a new method. Our approach 3D-LOTUS leverages rich 3D information for action prediction conditioned on language. While 3D-LOTUS excels in both efficiency and performance on seen tasks, it struggles with novel tasks. To address this, we present 3D-LOTUS++, a framework that integrates 3D-LOTUS's motion planning capabilities with the task planning capabilities of LLMs and the object grounding accuracy of VLMs. 3D-LOTUS++ achieves state-of-the-art performance on novel tasks of GemBench, setting a new standard for generalization in robotic manipulation. The benchmark, codes and trained models are available at \url{https://www.di.ens.fr/willow/research/gembench/}.

本研究针对缺乏适当模拟基准测试的问题，提出了GemBench，一个新颖的基准，用于评估视觉-语言机器人操作策略的推广能力。通过3D-LOTUS和3D-LOTUS++方法，该研究实现了在GemBench上新任务的先进性能，设立了机器人操作领域的推广新标准。

朝向可推广的视觉-语言机器人操作：基准测试和大型语言模型引导的三维策略