Virtual End-to-end integration
Unlock Your Logistics Potential
with LogiBot.Data
with LogiBot.Data
Why LogiBot.Data?
Empower Your Decision Making
Deploying humanoids without proper simulation leads to costly failures. AI models must be trained and validated before real-world use.
Warehouses need a risk-free environment to test humanoid performance before investment.
Warehouses need a risk-free environment to test humanoid performance before investment.
LogiBot.Data: Simulation Platform
Humanoids require data. Foundation models mine data from the web, utilising a significant fraction of all available documents. However, there is a lack of robot data so to enable a robot to learn a new skill.
Large amounts of data need to be collected with that particular robot and for that particular application. We developed a simulation platform to gain that
Large amounts of data need to be collected with that particular robot and for that particular application. We developed a simulation platform to gain that
- Digital Twin ModelingConverts real warehouse conditions into a virtual testing space.
- Robot Behavior TestingAssesses humanoid performance for navigation, grasping, task execution.
- Synthetic Training Data GenerationCreates edge cases and failure scenarios to improve AI adaptability.
Benefits
Chose Yours
Custom AI training simulations for humanoid robots
Warehouse-specific AI optimization before real-world deployment
Performance metrics & success probability analysis
How It Works
Create a virtual simulation of the environment to test humanoid models before real deployment.
1
Discovery
On-site visit to understand business needs
2
Flows
Identification of key pain points, inefficiencies, and cost drivers.
3
KPIs
Capture real-world metrics (task speed, accuracy, precision, human-robot interaction).
4
Pre-Processing
Select the robot hardware stack and required AI capabilities. Provide all operational data needed
5
Simulation
Transforming of the environment into RGBD data for AI-powered simulation.
Testing different AI strategies (vision, navigation, grasping, routing) in a virtual setting.
Selection optimal humanoid configurations
Validation of the AI capabilities
Testing different AI strategies (vision, navigation, grasping, routing) in a virtual setting.
Selection optimal humanoid configurations
Validation of the AI capabilities
6
Post-Analysis
Analyse obtained results and launch the service virtually
Assess Upfront
Success KPIs
Time & Cost Savings
Reduction in AI fine-tuning & deployment cost compared to traditional solutions.
Return on Investment (ROI)
Pilot vs. estimated cost savings over 6-12 months.
Scalability Index
Readiness to scale to new sites, robots, and integrations.
Task Completion Rate
% of successfully completed tasks by robots vs. failed attempts.
Time-to-Task (TTT)
Average time taken for a robot to complete a task.
Energy Efficiency
Energy needed for actual performing in a work chain
Try LogiBot Now!
