# SlotVLA: Towards Modeling of Object-Relation Representations in Robotic Manipulation arXiv: https://arxiv.org/abs/2511.06754 · HTML: https://mlsanity.com/paper/2511.06754/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Builds on 39 of its 41 references that are in the map. - First posted on arXiv 10 Nov 2025. ## Builds on (most cited first, 30 of 39) - [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021) - [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023) - [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020) - [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023) - [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014) - [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023) - [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023) - [OpenVLA: An Open-Source Vision-Language-Action Model](https://mlsanity.com/paper/2406.09246/) (2024) - [π0: A Vision-Language-Action Flow Model for General Robot Control](https://mlsanity.com/paper/2410.24164/) (2024) - [LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning](https://mlsanity.com/paper/2306.03310/) (2023) - [Grounded Language-Image Pre-training](https://mlsanity.com/paper/2112.03857/) (2021) - [Object-Centric Learning with Slot Attention](https://mlsanity.com/paper/2006.15055/) (2020) - [FAST: Efficient Action Tokenization for Vision-Language-Action Models](https://mlsanity.com/paper/2501.09747/) (2025) - [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://mlsanity.com/paper/2202.11094/) (2022) - [robosuite: A Modular Simulation Framework and Benchmark for Robot Learning](https://mlsanity.com/paper/2009.12293/) (2020) - [LLaVA-PruMerge: Adaptive Token Reduction for Efficient Large Multimodal Models](https://mlsanity.com/paper/2403.15388/) (2024) - [Robotic Control via Embodied Chain-of-Thought Reasoning](https://mlsanity.com/paper/2407.08693/) (2024) - [A Survey on Vision-Language-Action Models for Embodied AI](https://mlsanity.com/paper/2405.14093/) (2024) - [TokenPacker: Efficient Visual Projector for Multimodal LLM](https://mlsanity.com/paper/2407.02392/) (2024) - [SpatialBot: Precise Spatial Understanding with Vision Language Models](https://mlsanity.com/paper/2406.13642/) (2024) - [Scaling Proprioceptive-Visual Learning with Heterogeneous Pre-trained Transformers](https://mlsanity.com/paper/2409.20537/) (2024) - [VIOLA: Imitation Learning for Vision-Based Manipulation with Object Proposal Priors](https://mlsanity.com/paper/2210.11339/) (2022) - [6-DoF Pose Estimation of Household Objects for Robotic Manipulation: An Accessible Dataset and Benchmark](https://mlsanity.com/paper/2203.05701/) (2022) - [Improving Object-centric Learning with Query Optimization](https://mlsanity.com/paper/2210.08990/) (2022) - [Tokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving](https://mlsanity.com/paper/2407.00959/) (2024) - [Learning to See before Learning to Act: Visual Pre-training for Manipulation](https://mlsanity.com/paper/2107.00646/) (2021) - [Deep Object-Centric Representations for Generalizable Robot Learning](https://mlsanity.com/paper/1708.04225/) (2017) - [Matryoshka Query Transformer for Large Vision-Language Models](https://mlsanity.com/paper/2405.19315/) (2024) - [ControlVLA: Few-shot Object-centric Adaptation for Pre-trained Vision-Language-Action Models](https://mlsanity.com/paper/2506.16211/) (2025) - [Accelerating Transformers with Spectrum-Preserving Token Merging](https://mlsanity.com/paper/2405.16148/) (2024) ## Built on by (most cited first, 9 of 9) - [OA-WAM: Object-Addressable World Action Model for Robust Robot Manipulation](https://mlsanity.com/paper/2605.06481/) (2026) - [OFlow: Injecting Object-Aware Temporal Flow Matching for Robust Robotic Manipulation](https://mlsanity.com/paper/2604.17876/) (2026) - [StructSAM: Structure- and Spectrum-Preserving Token Merging for Segment Anything Models](https://mlsanity.com/paper/2603.07307/) (2026) - [FOCA: Future-Oriented Conditioning for Data-Efficient Vision-Language-Action Adaptation](https://mlsanity.com/paper/2606.20867/) (2026) - [Look Where It Matters: Adaptive Visual Refinement for Vision-Language-Action Models](https://mlsanity.com/paper/2608.02197/) (2026) - [Self-Improving VLA Policies: Selected Diffusion Noise for Spurious-Robust Action Smoothing](https://mlsanity.com/paper/2606.14084/) (2026) - [EmbodimentSemantic: A Spatial Scene-Graph Dataset and Benchmark for Vision-Language Models on Embodied Manipulation Trajectories](https://mlsanity.com/paper/2607.00020/) (2026) - [SmoLSTM: A Compact Vision-Language-Action Model with Recurrent Memory that Persists](https://mlsanity.com/paper/2609.22854/) (2026) - [Object-Centric Conditioning for Visuomotor Flow Matching](https://mlsanity.com/paper/2609.24155/) (2026) ## Abstract (arXiv) Inspired by how humans reason over discrete objects and their relationships, we explore whether compact object-centric and object-relation representations can form a foundation for multitask robotic manipulation. Most existing robotic multitask models rely on dense embeddings that entangle both object and background cues, raising concerns about both efficiency and interpretability. In contrast, we study object-relation-centric representations as a pathway to more structured, efficient, and explainable visuomotor control. Our contributions are two-fold. First, we introduce LIBERO+, a fine-grained benchmark dataset designed to enable and evaluate object-relation reasoning in robotic manipulation. Unlike prior datasets, LIBERO+ provides object-centric annotations that enrich demonstrations with box- and mask-level labels as well as instance-level temporal tracking, supporting compact and interpretable visuomotor representations. Second, we propose SlotVLA, a slot-attention-based framework that captures both objects and their relations for action decoding. It uses a slot-based visual tokenizer to maintain consistent temporal object representations, a relation-centric decoder to produce task-relevant embeddings, and an LLM-driven module that translates these embeddings into executable actions. Experiments on LIBERO+ demonstrate that object-centric slot and object-relation slot representations drastically reduce the number of required visual tokens, while providing competitive generalization. Together, LIBERO+ and SlotVLA provide a compact, interpretable, and effective foundation for advancing object-relation-centric robotic manipulation.