Towards Generalizable Vision-Language Robotic Manipulation: A Benchmark and LLM-guided 3D Policy
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Abstract (arXiv)
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 https://www.di.ens.fr/willow/research/gembench/.
Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). Builds on 49 of its 58 references that are in the map. First posted on arXiv 2 Oct 2024.
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Citing papers per year: 2025: 20; 2026: 17.
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- 2026MAGMA-GEN: Validated Recovery Supervision from Ambiguous Failures via Counterfactual Re-Execution17 Sep 2026
- 2026Bench2Dex: Benchmarking Visuo-Tactile Bimanual Dexterous Manipulation Across Dexterous Hands14 Sep 2026
- 2026Decompose and Recompose: Reasoning New Skills from Existing Abilities for Cross-Task Robotic Manipulation2 May 2026
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