Towards Generalizable Vision-Language Robotic Manipulation: A Benchmark and LLM-guided 3D Policy
<|reference_start|>Towards Generalizable Vision-Language Robotic Manipulation: A Benchmark and LLM-guided 3D Policy: 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/}.<|reference_end|>
@article{garcia2024towards,
    title={Towards Generalizable Vision-Language Robotic Manipulation: A Benchmark
  and LLM-guided 3D Policy},
    author={Ricardo Garcia, Shizhe Chen, Cordelia Schmid},
    journal={arXiv preprint arXiv:2410.01345},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.01345},
    primaryClass={cs.RO cs.CV}
}