SlotVLA: Towards Modeling of Object-Relation Representations in Robotic Manipulation
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866911650945171456 |
|---|---|
| author | Hanyu, Taisei Chung, Nhat Le, Huy Nguyen, Toan Ikebe, Yuki Gunderman, Anthony Minh, Duy Nguyen Ho Vo, Khoa Kieu, Tung Yamazaki, Kashu Rainwater, Chase Nguyen, Anh Le, Ngan |
| author_facet | Hanyu, Taisei Chung, Nhat Le, Huy Nguyen, Toan Ikebe, Yuki Gunderman, Anthony Minh, Duy Nguyen Ho Vo, Khoa Kieu, Tung Yamazaki, Kashu Rainwater, Chase Nguyen, Anh Le, Ngan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_06754 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SlotVLA: Towards Modeling of Object-Relation Representations in Robotic Manipulation Hanyu, Taisei Chung, Nhat Le, Huy Nguyen, Toan Ikebe, Yuki Gunderman, Anthony Minh, Duy Nguyen Ho Vo, Khoa Kieu, Tung Yamazaki, Kashu Rainwater, Chase Nguyen, Anh Le, Ngan Robotics Computer Vision and Pattern Recognition 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. |
| title | SlotVLA: Towards Modeling of Object-Relation Representations in Robotic Manipulation |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.06754 |