Generalist robot manipulation requires a single vision-language-action (VLA) policy to execute diverse skills across scenes and instructions. However, current multi-skill VLA policies often leave two aspects of an instruction insufficiently modeled: which primitive skill to execute and where/how to contact and move objects. We propose MAST-VLA (Multi-skill Adaptation with Skill-conditioned experts and Trajectory-affordance priors), a dual-conditioning framework for robot manipulation. MAST-VLA introduces skill-conditioned LoRA Mixture-of-Experts (LoRA-MoE) adapters in the LLM backbone, where skill-query tokens and routing biases guide low-rank expert specialization, and converts a predicted affordance heatmap into a patch-level trajectory-affordance spatial prior for the action head. On the CALVIN ABC→D long-horizon multi-skill cross-scene generalization setting, MAST-VLA achieves state-of-the-art performance, reaching an Avg. Len of 4.65 over five-subtask evaluation sequences. Real-robot multi-skill sequence experiments further validate its deployability under real perception and control noise.
Figures & tables
Figure 1: Overview of MAST-VLA. MAST-VLA introduces two complementary conditioning paths for multi-skill VLA policies: skill-conditioned LoRA-MoE adaptation in the LLM backbone and a trajectory-affordance spatial prior in the action head. The expanded LLM-block view shows FFN-side LoRA-MoE routing with skill-conditioned routing bias, while the action-head view shows how the predicted affordance heatmap is converted into a patch-level spatial prior for action generation.
Method
Tasks Completed in a Row (%)
Avg. Len ↑
1
2
3
4
5
OpenVLA [ 17 ]
91.3
77.8
62.0
52.1
43.5
3.27
UniVLA [ 6 ]
95.5
85.8
75.4
66.9
56.5
3.80
PointMapPolicy [ 15 ]
96.1
88.6
80.5
72.3
63.6
4.01
EL3DD [ 4 ]
96.6
90.0
82.4
74.2
66.2
4.09
OpenVLA-OFT [ 16 ]
96.3
89.1
82.4
75.8
66.5
4.10
Table 1: Long-horizon robotic manipulation evaluation on CALVIN ABC → D.
Figure 2: Representative real-robot evaluation tasks. Representative test-time primitive subtasks are shown for two tabletop scenes. The examples cover diverse interaction modes, including nesting, pick-and-place, pushing, stacking, and insertion.
Figure 3: Real-robot sequence-completion results. (a) Per-sequence Avg. Len across eight length-3 sequences in two tabletop scenes. (b) Success@1–3 comparison over consecutive primitive completion. (c) Overall Avg. Len comparison across all real-robot episodes.
Query
LoRA-MoE
Aff. Heatmap
Skill Cond.
Tasks Completed in a Row (%)
Avg. Len ↑
1
2
3
4
5
–
98.5
95.0
90.5
85.3
80.0
4.50
✓
matched
98.7
95.6
91.3
86.4
81.1
4.53
✓
✓
matched
98.8
96.3
92.8
88.0
83.1
4.59
✓
–
99.0
96.7
93.4
88.6
83.7
4.61
✓
✓
✓
matched
99.4
97.4
94.2
89.2
84.7
4.65
Table 2: Ablation on CALVIN ABC → D. Query: skill-query tokens. LoRA-MoE: skill-conditioned low-rank experts. Aff. Heatmap: trajectory-affordance spatial prior. Skill Cond.: matched uses the instruction-derived primitive skill condition; mismatched replaces it with a random non-matching condition at test time.
Figure 4: Skill-conditioned Top-2 LoRA-MoE routing signatures. Expert-usage shifts across primitive skills and token groups are measured in percentage points (pp) relative to average usage. Stars/circles denote the experts with the highest/second-highest average routing usage for each skill; they indicate learned routing preferences rather than fixed skill-to-expert assignments.
Figure 5: RGB observations and predicted affordance heatmaps are shown across representative manipulation skills.
Appendix figures & tables5 assets
Supplementary material from the paper’s appendix.
Appendix
Skill ID
Primitive skill condition
Representative fine-grained commands
0
Rotation
Rotate red/blue/pink blocks left or right.
1
Sliding / pushing
Push red/blue/pink blocks left or right; push tabletop objects to target regions.
2
Articulated-object operation
Move the slider left or right; open or close the drawer; close articulated containers.
3
Lifting / picking
Lift red/blue/pink blocks to the table, slider, or drawer; pick tabletop objects.
4
Placing / storing
Place objects onto the slider, into the drawer/box, or in other target regions.
5
Stacking / nesting
Stack or unstack blocks/erasers; nest disposable cups in real-robot sequences.
Appendix
Table 3: Primitive skill taxonomy and skill-condition ids used by MAST-VLA.
Item
Setting
Base policy
VLA-Adapter-Pro initialized from prism-qwen25-extra-dinosiglip-224px-0_5b .
Language backbone
Qwen2.5-0.5B, hidden dimension 896.
Visual input
Static third-person RGB and wrist/secondary RGB, resized to 224×224 .
Visual tokens
256 patch tokens per image; 512 visual tokens for two-view input.
Action representation
Continuous action chunk with horizon H=8 ; each 7-D action contains 6-DoF end-effector motion and one gripper command.
Action query tokens
64 learnable action tokens.
Appendix
Table 4: Main implementation settings of MAST-VLA.
Item
Setting
Training data
CALVIN ABC training split.
Training schedule
50k MAST-VLA fine-tuning updates.
GPUs
8 GPUs with distributed data parallel training.
Batch size
16 per GPU, gradient accumulation 1.
Optimizer
AdamW with parameter groups.
LoRA learning rate
2×10−4 .
Appendix
Table 5: Training hyperparameters for the main MAST-VLA run.
Scene
Seq.
Primitive instruction sequence
Scene 1
1
Pick cup → Nest cup → Push box
Scene 1
2
Stack eraser → Push box → Pick cup
Scene 1
3
Pick marker → Place marker → Stack eraser
Scene 1
4
Push box → Pick marker → Place marker
Scene 2
1
Pick flower → Insert flower → Stack blocks
Scene 2
2
Stack blocks → Unstack blocks → Push mango
Appendix
Table 6: Real-robot length-3 task sequences used in evaluation.
Scene
Seq.
Method
Tasks Completed in a Row (%)
Avg. Len ↑
1
2
3
Scene 1
1
OpenVLA-OFT
90
70
15
1.75
π0
100
85
50
2.35
π0.5
100
90
65
2.55
VLA-Adapter-Pro
95
85
30
2.10
MAST-VLA
100
85
85
2.70
Appendix
Table 7: Per-sequence real-robot evaluation results. Each method has 20 trials per sequence and 160 trials in total. Success rates are percentages and Avg. Len is out of 3.