Coupling a pretrained vision-language model (VLM) with a diffusion or flow-matching action expert has become the mainstream vision-language-action (VLA) paradigm for embodied policies, yet they remain unreliable once a task demands precise manipulation, e.g., submillimeter cable insertion. We attribute this weakness to a training objective that averages flow-matching errors across contexts and flow times, allocating little supervision to the few decisive moments determining task success. Along the generative path, inference integrates a locally supervised velocity field, so equally penalized errors at different flow times can affect the executed action unequally. Consequently, precision-task performance deteriorates as physical tolerance tightens by orders of magnitude. We call this supervision imbalance across contexts and flow times Flow-Time Starvation. The remedy is to jointly align supervision allocation and representation learning with context-dependent precision demand, enabling the policy to recognize when greater precision is needed. We instantiate this precision-forcing strategy as Precision-aware Reweighting and Internalization for Skilled Manipulation (PRISM), which scores precision demand offline, strengthens flow supervision where errors are consequential and action signals are visible, and internalizes that demand in the VLM condition representation. Simulation and real-robot experiments show that PRISM preserves or improves general-task performance while raising mean end-to-end success from 38.3% to 86.7% across drive connector insertion, bimanual connector mating, and screw tightening, substantially extending the precision boundary of existing embodied policies without new sensors or observation modalities.
Figures & tables
Figure 1: Overview of Prism . PPS estimates precision demand from robot trajectories. CPR learns precision-aware VLM features, while PSW focuses action supervision on precision-demanding contexts with visible action evidence, improving fine manipulation without changing policy inference.
Figure 2: Why precise manipulation needs different supervision. Left, top to bottom: key frames and tolerances for cable pickup, bimanual handover, and connector insertion, the three stages of the real-robot submillimeter drive-insertion task. (a) Similar action errors incur different tolerance-normalized burdens. (b) Inverse-tolerance-weighted endpoint responses to equal-energy velocity perturbations peak near the clean endpoint for fine manipulation.
Figure 3: Overview of Prism . (a) PPS combines joint motion, end-effector motion, and gripper engagement into offline precision demand. (b) CPR predicts this demand from mask-pooled VLM prefix features, while PSW reweights the flow-matching loss using offline demand and flow-time visibility derived from action RMS and flow time. Both objectives leave inference unchanged.
Method
Close box
Close laptop
Toilet seat
Sweep
Close fridge
Phone
Umbrella
Frame
Wine
Water plants
Mean
OpenVLA
0.60
0.35
0.75
0.55
0.85
0.20
0.30
0.15
0.20
0.05
0.40
SpatialVLA
0.80
0.70
0.85
0.20
0.80
0.15
0.25
0.40
0.15
0.30
0.46
π0
0.90
0.60
1.00
0.30
0.90
0.25
0.35
0.75
0.05
0.45
0.55
CogACT
0.90
0.80
0.95
0.50
0.85
0.50
0.55
0.45
0.30
0.25
0.61
CoT-VLA
0.95
0.75
1.00
0.80
0.65
0.50
0.40
0.50
0.55
0.50
0.66
HybridVLA
0.85
0.95
1.00
0.90
1.00
0.50
0.50
0.70
0.50
0.50
0.74
Table 1: RLBench-10 success rates (absolute gains in parentheses). Green highlights Prism .
Figure 4: Representative stages of three MSSM tasks, ordered temporally from left to right.
Method
Drive connector insertion (0.15 mm)
Bimanual connector mating (0.1 mm)
Screw tightening (0.2 mm)
Avg.
S1
S2
S3
SR ↑
T↓
S1
S2
S3
S4
S5
SR ↑
T↓
S1
S2
S3
SR ↑
T↓
SR ↑
π0.5
1.00
0.90
0.35
0.35
47
0.95
0.95
0.50
0.55
0.65
0.35
65
0.95
0.85
0.45
0.45
37
0.38
π0.5 + DAgger
1.00
1.00
0.65
0.65
43
0.95
1.00
0.70
0.75
0.85
0.60
59
1.00
0.95
0.70
0.70
35
0.65
π0.5 + Prism
1.00
1.00
0.95
0.95
38
1.00
1.00
0.85
0.90
0.95
0.80
52
1.00
1.00
0.85
0.85
32
0.87
Table 3: MSSM real-robot results. S1–S5 report stage success. SR is end-to-end task success. T is mean completion time in seconds over successful trials. Avg. averages SR over the three tasks.
Table 7
Figure 5: Mechanism analysis of precision forcing. (a) CPR predictions versus offline PPS on held-out insertion trajectories. (b) Residual reduction over π0.5 across five equal-count PPS bins. (c) Residual profiles in four equal-count SNR groups, formed per flow time within high-PPS frames. Purple/teal indicates a decrease/increase under PSW. (d) Relative CPR-conditioned visual sensitivity across fine-manipulation contexts.
Appendix figures & tables6 assets
Supplementary material from the paper’s appendix.
Appendix
Setting
StarVLA
π0.5
VLM backbone
Qwen3-VL-4B-Instruct
PaliGemma (Gemma 2B)
Action expert
QwenPI_v3, 36 layers, width 1,024
Gemma 300M
Input image resolution
224×224
224×224
Robot state input
Yes
Yes
Action horizon H
15
15
Action dimensions (dataset-specific)
8 (single-arm RLBench)
16 (MSSM); padded to 32
Appendix
Table 6: Training configurations, real-robot execution settings, and offline PPS efficiency. PPS measurements use insertion trajectories and are independent of the policy backbone.
Task
Objects and specifications
Drive connector insertion
Geek+ GS-SD-48V-20A-21D V01.01 dual-channel safety motor drive and a Hirose DF22 cable assembly with a matching drive receptacle.
Bimanual connector mating
Three connector pairs, used in order: white Hirose DF22 2-pin (0.2 mm tolerance), white Hirose DF22 4-pin (0.1 mm), and black Hirose DF60 (0.3 mm). Male halves start on the left and female halves on the right.
Screw tightening
Bosch GO Gen 2 cordless screwdriver with a compatible bit; a loose screw pre-positioned in the lower-left threaded hole of the motor-drive housing. Measured alignment tolerance: 0.2 mm.
Appendix
Table 7: Objects used in the three MSSM tasks.
Task
Stage
Description
Drive connector insertion
S1: Cable pickup
The left arm picks up the cable behind its connector housing.
S2: Bimanual handover
The left arm transfers the cable to the right arm.
S3: Connector insertion
The right arm aligns the cable connector with the receptacle and inserts it.
Bimanual connector mating
S1: Left-arm pickup
The left arm grasps the male half of the first connector pair.
S2: Right-arm pickup
The right arm grasps the female half of the first connector pair.
S3: First pair
Align and mate the Hirose DF22 2-pin pair, then place it back on the workbench.
Appendix
Table 8: MSSM tasks, stages, and operation descriptions.
Figure 8: CPR sensitivity across three tasks and three camera views. Each column shows one task. Rows show the head, left-wrist, and right-wrist cameras at the same selected observation within that task. Labels identify the action and camera. Colors indicate relative within-view sensitivity, not a shared absolute response scale. Selected views and heatmaps are jointly rotated for readability.
Figure 9: PSW amplification on drive connector insertion. Success peaks at γ=3 .
Figure 10: Equal-energy perturbations across flow time. Curves show unweighted endpoint action MSE per unit perturbation energy on ten held-out episodes. Shaded bands are 95% episode-bootstrap intervals. The clean-end interval produces the largest response in each operation stage at both energy settings.