What is a vision language model and how does it help robotic manipulation?

A vision language model (VLM) processes scene images and understands natural language instructions simultaneously. For robotics, this means you can tell the system 'pick up the red connector from the left bin' instead of programming exact pixel coordinates. Datature Vi fine-tunes these models on your specific workspace layouts and object types.

How do VLMs differ from traditional robotic vision systems?

Traditional robotic vision requires separate object detectors, pose estimators, and grasp planners. A VLM trained on Datature Vi combines object recognition with spatial reasoning in a single model. It understands 'the bolt next to the gasket' without needing explicit spatial coordinates programmed for every object pairing.

Why should robotics teams fine-tune VLMs on Datature Vi?

Generic VLMs lack knowledge of your specific parts, bin layouts, and manipulation requirements. Datature Vi fine-tunes on your annotated workspace images, so the model learns the exact parts, orientations, and spatial relationships in your production environment. This domain-specific training is what makes VLM-guided manipulation reliable.

How much training data does Datature Vi need for pick and place tasks?

Start with 300 to 500 annotated images of your workspace covering different part arrangements, lighting conditions, and bin fill levels. Datature Vi's phrase grounding annotations link natural language part descriptions to their visual locations. IntelliScribe pre-labeling accelerates this process 3-5x.

Which base model works best for robotic vision on Datature Vi?

Qwen2.5-VL 7B with LoRA is the recommended starting point for scene understanding and object localization. For tasks requiring multi-step reasoning about manipulation sequences, Cosmos-Reason1 7B from NVIDIA offers built-in chain-of-thought capability that can break down complex pick instructions into sequential steps.

Can Datature Vi models output structured grasp coordinates?

Yes. Structured JSON output is built into every Vi fine-tuned model. The model outputs JSON with object identity, bounding box coordinates, and spatial relationship fields. Your robot controller parses these structured outputs directly into motion planning commands via Vi's OpenAI-compatible API.

How does Datature Vi handle cluttered bin scenarios?

Train phrase grounding annotations on cluttered bin images where objects overlap and occlude each other. The VLM learns to identify partially visible objects by combining visual features with contextual understanding. Datature Vi's chain-of-thought annotations teach the model to reason about occlusion ('the blue connector is partially hidden behind the bracket').

Can Datature Vi process camera feeds in real time for robotics?

Yes. Deploy the Vi SDK on a local GPU server connected to your robot's camera. Inference runs under 100ms per frame, supporting real-time visual servoing for pick and place operations. Deploy on your existing robot controller hardware without additional vision-specific equipment.

How does Datature Vi evaluate robotic vision model performance?

Datature Vi reports IoU and F1 for object localization accuracy, plus BLEU and BERTScore for the quality of spatial reasoning descriptions. The side-by-side evaluation view lets you compare predicted object locations against ground truth, and the checkpoint scrubber shows localization accuracy improving across training epochs.

Can Datature Vi models handle multiple object types in mixed bins?

Yes. Phrase grounding supports multiple object descriptions per image. A single model trained on Datature Vi identifies bolts, washers, connectors, and brackets simultaneously, reporting each object's type, location, and spatial relationship to neighboring parts.

How does Datature Vi support chain-of-thought reasoning for multi-step tasks?

Datature Vi's annotation tools support chain-of-thought labels that teach the model step-by-step reasoning. For example: 'First locate the housing, then identify the open slot, then find the matching connector in the bin.' Cosmos-Reason1 7B is optimized for this multi-step reasoning capability.

Does Datature Vi work with existing robot control frameworks?

Datature Vi provides the vision layer, not the robot control stack. Deploy models via the Vi SDK on a local server, then feed structured outputs (object coordinates, classifications, spatial descriptions) into your ROS, MoveIt, or proprietary control framework through Vi's OpenAI-compatible API. The separation keeps your robot control logic independent.

Robotic Pick and Place AI with VLMs

THE CHALLENGE

THE PROBLEM.

Traditional robotic pick-and-place relies on rigid programming for each object type. New SKUs require reprogramming. Mixed-item bins defeat template-based approaches, and every new product variant means downtime.

4.2s

Total cycle time from visual detection to completed pick-and-place operation including grasp planning

Lines of new code required to handle previously unseen object types using natural language instructions

2.5N

Grip force dynamically calculated per object based on material properties, weight, and fragility

No Domain KnowledgeCan't Read ImagesFine-Tuned on Vi

THE BASELINE

GENERAL MODELS LACK DOMAIN EXPERTISE.

GPT-4o, Claude, and Gemini have broad knowledge, but zero understanding of your specific domain, standards, or terminology.

✗No actionable grasp data for the robot controller

Why it fails:

•Describes objects in rough spatial terms ("the bolt near the top") but no pixel-level grasp coordinates usable by a robot controller

•Classifies obvious parts (bolts, washers) but cannot determine orientation angles, occlusion depth, or pick-order accessibility

Foundation Model (GPT-4o / Claude / Gemini)

You

Prompt

Identify all graspable objects in this bin image and suggest pick order with grasp points

image_001.jpg

Response

I can see a bin containing various industrial parts including what appear to be bolts, washers, and cylindrical components. There are roughly 8-10 items visible. For robotic pick-and-pla

THE GAP

GENERAL MODELS CAN'T READ YOUR IMAGES.

Even with reference documents attached, foundation models cannot reliably interpret domain-specific visual data.

✗Object count is close but no usable coordinates

Why it fails:

•"8-12 objects" when actual count is 9 distinct parts

•Cannot determine which objects are occluded or stacked

Foundation Model + RAG (Context-Augmented)

You

Prompt + Reference Data

Identify graspable objects, classify parts, and suggest pick order with grasp coordinates

image_001.jpg

part_catalog_with_dimensions.pdf20_bin_picking_examples.zip

Response

Based on the part catalog, I can identify approximately 8-12 objects in the bin. The top-most objects appear to be hex bolts and possibly some washers. A reasonable pick order would be to s

THE ANSWER

YOUR DATA, FINE-TUNED ON VI.

A model trained on your private data sees exactly what you see. Your domain. Your standards. Production-ready.

✓Deployed on UR10e line. 340 picks/hour sustained.

96.2%

Pick Success

10.6s

Cycle Time

0.3%

Collision Rate

Datature Vi — Inference

LIVE

Model: ft-binpick-qwen7b-v3

MESH_EXTENDER[BOX] · 95.0%

CASCADE[PLASTIC_BOX] · 95.0%

SURGEPROTECTOR[BOX_WRAPPED] · 95.0%

Vi Output

3 graspable items detected in yellow tote. Pick order (collision-free): 1. MESH_EXTENDER[BOX] (bottom-left, 145,340) — rigid box, top-grasp, clearance 14mm. 2. CASCADE[PLASTIC_BOX] (center-top, 310,180) — cylindrical container, side-grasp at 0deg, clearance 9mm, heaviest item. 3. SURGEPROTECTOR[BOX_WRAPPED] (bottom-right, 480,360) — flexible packaging, top-grasp, clearance 11mm. Gripper: parallel jaw, 45mm aperture. Estimated cycle: 32s total.

96.2%

Pick Success

10.6s

Cycle Time

0.3%

Collision Rate

Scroll to continue

↓

SEE IT IN ACTION

YOUR OUTPUT, YOUR FORMAT.

Structured reports, raw JSON, concise alerts. Control the output with system prompts and refine it with RLHF. The model speaks the way your application needs it to.

Generate a bin picking report for this workspace image with object inventory, grasp strategies, and pick order

FAQ

ROBOTIC PICK & PLACE
FAQ.

Everything you need to know about using Datature Vi for Robotic Pick & Place.

GET STARTED

SEE IT
IN ACTION.

30-minute walkthrough of Datature Vi applied to Robotic Pick & Place. Bring your own dataset or use ours.

Schedule a Demo

Walk through the full pipeline with an engineer. Annotation, training, evaluation, and deployment for your specific use case. 30 minutes.

Start Free

3,000 data rows and 300 compute credits free every month. All annotation modes, all model architectures, Vi SDK access. No credit card.

All annotation modes included

Qwen2.5-VL, InternVL3.5, Cosmos

Vi SDK with 4-bit quantization

Get Started

Enterprise Ready

View Trust Center

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Audited annually

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Encrypted at rest and in transit

G2 High Performer

4.9/5 with 47 reviews

Your Data, Your Models

Full ownership and export

VISION-GUIDED ROBOTIC PICK AND PLACE

THE PROBLEM.

GENERAL MODELS LACK DOMAIN EXPERTISE.

GENERAL MODELS CAN'T READ YOUR IMAGES.

YOUR DATA, FINE-TUNED ON VI.

FROM RAW IMAGES TO
PRODUCTION MODEL.

Ground Language to Objects

Train Spatial Understanding

Stream to Robotic Arms

YOUR OUTPUT, YOUR FORMAT.

GUIDE YOUR ROBOTS WITH LANGUAGE.

ROBOTIC PICK & PLACE
FAQ.

SEE IT
IN ACTION.

Schedule a Demo

Start Free

RELATED USE CASES.

Warehouse Intelligence

Quality Inspection

Safety Monitoring

TRY THIS USE CASE.
START FREE.

VISION-GUIDED ROBOTIC PICK AND PLACE

THE PROBLEM.

GENERAL MODELS LACK DOMAIN EXPERTISE.

GENERAL MODELS CAN'T READ YOUR IMAGES.

YOUR DATA, FINE-TUNED ON VI.

FROM RAW IMAGES TOPRODUCTION MODEL.

Ground Language to Objects

Train Spatial Understanding

Stream to Robotic Arms

YOUR OUTPUT, YOUR FORMAT.

GUIDE YOUR ROBOTS WITH LANGUAGE.

ROBOTIC PICK & PLACEFAQ.

What is a vision language model and how does it help robotic manipulation?

How do VLMs differ from traditional robotic vision systems?

Why should robotics teams fine-tune VLMs on Datature Vi?

SEE ITIN ACTION.

Schedule a Demo

Start Free

RELATED USE CASES.

Warehouse Intelligence

Quality Inspection

Safety Monitoring

TRY THIS USE CASE.START FREE.

FROM RAW IMAGES TO
PRODUCTION MODEL.

ROBOTIC PICK & PLACE
FAQ.

SEE IT
IN ACTION.

TRY THIS USE CASE.
START FREE.