SlotVLA: Towards Modeling of Object-Relation Representations in Robotic Manipulation
Authors: Taisei Hanyu, Nhat Chung, Huy Le, Toan Nguyen, Yuki Ikebe, Anthony Gunderman, Duy Nguyen Ho Minh, Khoa Vo, +5 more
Organizations: University of Arkansas, USA · FPT Software AI Center, Vietnam · University of Stuttgart, Germany · German Research Center for Artificial Intelligence, Germany · Max Planck Research School for Intelligent Systems, Germany · Aalborg University, Denmark · Carnegie Mellon University, USA · University of Liverpool, UK
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.
Figures & tables
Fig. 1: Comparison of visuomotor tokenization strategies. (a) Dense tokenizers generate hundreds of tokens across the scene, leading to computationally costly representations. (b) Object-centric tokenizer yields NK tokens, each representing an object. (c) Our object–relation-centric tokenizer yields NS object tokens and NR relation tokens, producing structured and efficient representations. Plot (2) shows that our method achieves higher success rates with fewer tokens compared to baselines on LIBERO-Goal.
Fig. 2: Overview of the LIBERO+ dataset.
Statistics
LIBERO+
L-Object
L-Goal
L-Spatial
L-Long
# Tasks
10
10
10
10
# Object Layouts
1
1
10
9
# Objects
12
7
11
29
# TR Objects
2–3
2–3
3–4
3–4
# Total Frames
72,063
54,779
47,253
84,896
TABLE I: Statistics of LIBERO+. TR corresponds to Task-relevant objects.
Fig. 3: Overall framework of our proposed model. Stage-1 trains the Task-aware Object-Centric Encoder with slot attention and task-aware filtering. Stage-2 freezes Stage-1 parameters and introduces the Relation-Centric Encoder, enabling relational reasoning for final action decoding.
Fig. 4: Slot decomposition result. Task query: “ Put the bowl on the stove ”. Task-relevant slots correctly bind to objects, while irrelevant slots scatter.
LIBERO-Goal
LIBERO-Spatial
LIBERO-Object
LIBERO-Long
OpenVLA
OC
ORC
OpenVLA
OC
ORC
OpenVLA
OC
ORC
OpenVLA
OC
ORC
Average
0.77
0.77
0.86
0.72
0.48
0.60
0.70
0.90
0.91
0.56
0.12
0.31
No. Token
256
4 ( 64 × )
20 ( 13 × )
256
4 ( 64 × )
28 ( 9 × )
256
4 ( 64 × )
28 ( 9 × )
256
4 ( 64 × )
28 ( 9 × )
GFLOPs
2,112
561 ( 4 × )
697 ( 3 × )
2,112
568 ( 4 × )
723 ( 3 × )
2,112
568 ( 4 × )
723 ( 3 × )
2,112
568 ( 4 × )
723 ( 3 × )
Task 1
0.60
0.90
0.70
0.82
0.70
0.65
0.75
0.85
0.95
0.75
0.50
0.20
Task 2
0.95
0.50
0.75
0.95
0.90
0.20
0.70
0.80
0.80
0.90
0.00
0.00
TABLE II: Benchmark on LIBERO+ consisting of four subsets from LIBERO. Highest results are bolded , second-highest are underlined . No. Token indicates the number of tokens used. The tasks, numbered from 1 to 10, are specific to each subset and sorted alphabetically.
LIBERO-Goal
LIBERO-Spatial
LIBERO-Object
LIBERO-Long
OC
ORC
OC
ORC
OC
ORC
OC
ORC
Language
✗
✓
✗
✓
✗
✓
✗
✓
✗
✓
✗
✓
✗
✓
✗
✓
Average
0.77
0.77
0.72
0.86
0.53
0.48
0.60
0.60
0.76
0.90
0.91
0.91
0.11
0.07
0.12
0.31
Task 1
0.75
0.90
0.60
0.70
0.75
0.70
0.80
0.65
0.95
0.85
0.75
0.95
0.05
0.15
0.50
0.20
Task 2
1.00
0.50
0.40
0.75
0.70
0.90
0.20
0.20
0.40
0.80
0.85
0.80
0.00
0.00
0.00
0.00
Task 3
0.95
0.90
0.70
1.00
0.15
0.00
0.75
0.40
1.00
1.00
0.85
1.00
0.25
0.00
0.10
0.40
TABLE III: Ablation study on Task-Aware Slot Filtering. Object-centric slots (OC) and object–relation-centric slots (ORC) are compared. ✓ indicates that filtering is included, while ✗ indicates that filtering is not included. The tasks, numbered from 1 to 10 are specific to each subset and sorted alphabetically.
Method
4 tokens
8 tokens
16 tokens
OC
0.77
0.65
0.77
ORC
0.86
0.74
0.72
TABLE IV: Ablation study on number of object tokens.
Fig. 5: Trajectory demonstration in simulation from exocentric views. Task query: “Put the bowl on the stove”.
Method
Temporal Consistency
✗
✓
OC
0.38
0.77
ORC
0.40
0.86
TABLE V: Ablation study on the effect of temporal consistency.