From foundation models and simulated worlds to working robots.
We bring together world models, foundation models, simulation, and robot software. Model the task, train or adapt the policy, test it in simulation, then integrate it with OEM-supplied robots at your site alongside qualified partners.
A simulated workcell meets its real factory reference at the moment of placement. A visual sim-to-real comparison; policy training and hardware validation are separate engineering stages.
Existing models and robots are the starting point. We connect them around the task, build the environment to develop in, and carry the application through evaluation and physical deployment.
Build the task environment around robot geometry, sensors, and physics. Use world-model tools such as NVIDIA Cosmos for suitable visual-data and scenario workflows.
Simulation environment + task specificationA pretrained VLA can already be the policy. Fine-tune a compatible model with teleoperation demonstrations; add simulation-based learning where appropriate.
Versioned policy + learning configurationRun inference with a fixed policy in repeatable simulation scenarios. Vary conditions, review failures, and measure the sim-to-real gap through hardware trials.
Evaluation record + transfer criteriaRun inference with fixed weights on live sensor inputs. Map actions to the OEM controller, validate timing and stop behavior, and improve future versions with operational feedback.
Runtime integration + operating feedbackA robot policy turns observations into actions. We develop the policy and the software that connects it to real sensors, controllers, and operating conditions.
The task profile accompanies that work: one specification linking the robot, environment, model configuration, test evidence, and deployment limits.
See how a policy reaches the robot ↗Example project specification. No training result or customer deployment is implied.
One reference task.
A world to inspect.
A cobot picks up cartons and places them on a pallet in PESTOPACK’s factory footage. The workcell layout, camera view, and movement provide the reference.
A simulated workcell represents the robot, conveyor, cartons, and pallets from the physical reference. This visual simulation shows the task and layout; a deployment also requires calibrated dynamics, sensors, and robot interfaces.
Combine teleoperation demonstrations with simulation, fine-tune a compatible foundation-model policy, and evaluate it before running inference on OEM hardware. Physical trials and failure feedback help close the sim-to-real gap.
This example illustrates the relationship between a physical task and its simulated representation. It does not demonstrate a trained policy transferring to hardware. Read the source and editing notes ↗
We help you select a useful starting point, connect existing hardware and AI, evaluate the application, and support deployment with qualified partners.
Explore our robotics engineering ↗See the operation.
Engineer the right intervention.
Layout, movement, and the people around the task.
Robots, perception, tooling, and adapted intelligence.
Systems integration, acceptance, and continuous improvement.
Agree on the part, defect, throughput target, and intervention rules.
A task specification and baseline.Represent the robot, camera, tooling, and working envelope. Check the assumptions that need physical testing.
A workcell model and interface plan.Connect perception and task software. Evaluate established models and demonstration data where they fit the workflow.
A task adaptation and evaluation plan.Test defect detection, handling, exceptions, and the limits of the simulated environment.
Results against agreed acceptance criteria.Integrate site systems and support a bounded pilot with qualified hardware and deployment partners.
An agreed commissioning and handoff plan.Record outcomes and interventions. Investigate failures and prioritize the next engineering change.
An evidence record and improvement backlog.Illustrative application path. Hardware suitability, performance, site requirements, and partner responsibilities are evaluated for each engagement.
Connect signals from the physical world.
Turn operational data into context.
Build reliable decisions into everyday operations.
Explore the wider robotics ecosystem through these publisher films. These are industry references, separate from DehazeLabs work.
Simulation, model adaptation, and robotics integration lead the work. Infrastructure and applied AI connect the application to your wider operation.
↗Task profiles, simulated environments, policy adaptation, and evaluation—followed by deployment on OEM hardware with qualified partners.
↗GPU infrastructure, data platforms, distributed systems, telemetry, observability, and production MLOps.
Computer vision, multimodal applications, agents, edge inference, model adaptation, and custom enterprise software.
Customer work across perception, data, infrastructure, and applied AI supports our robotics practice. Each case retains its own scope and delivery stage.
Exported scan comparisons · approximately 10× playback
Simulation telemetry, not camera footage or a product UI recording.
The cached triage report flags a localization-degradation hypothesis. An engineer still needs to compare the signals, fill the evidence gaps, and validate the cause.
The model reported medium confidence. That is not a performance score. The saved artifacts do not establish a confirmed cause, human approval, or an improvement in robot performance.
The source report asks for additional scan/map, odometry, and costmap context. The plots shown here are separate exported reference artifacts.
Explore the tools we build for infrastructure, business knowledge, and robot operations.
Explore how Company Brain connects a business question to the records behind it.
02 / InfrastructureFollow a readiness review across compute, network, power, and cooling.
03 / Physical AISee how raw robot logs become evidence a reviewer can turn into a curated dataset.
Define the task and model the environment. Train or adapt a policy, evaluate a fixed version, and integrate its inputs and outputs with the selected OEM robot. Hardware trials and site acceptance connect simulation results to real operations.
Models, simulation, policy adaptation, application software, and evaluation.
Robot hardware, tooling, site integration, and commissioning within the agreed scope.
Workflow knowledge, site access, operational ownership, and acceptance decisions.
Our engineers embed directly into your team. From architecture and data infrastructure to deployment and ongoing operation, we own the work with you.
US-based partnership leadership. Engineering depth across Hyderabad and Bengaluru.
How we work ↗Connect knowledge, infrastructure, and workflows around a defined application. Our engineering teams connect the application. Robot Ops, EdgeTelemetry, and Company Brain provide purpose-built tools for robot operations, infrastructure, and business knowledge.
An assessment turns an operational idea into a task profile, a model and simulation plan, and the evidence needed for a pilot decision.
The workflow, robot context, operating constraints, and baseline.
Foundation models, OEM hardware, sensors, and integration needs.
Environment assets, demonstrations, scenario coverage, and technical unknowns.
What to measure and how to make the go/no-go decision.
Scope is agreed before work begins. An assessment can recommend a pilot, further investigation, or a decision not to automate.
