Robotics Services
Most robotics projects need the same four things to ship. Software, perception, simulation, and hardware. We work on all four. You can engage us for one layer or the whole stack. Below is what we do in each area, and the open-source and commercial tools we use.
New here? Read how we approach a robotics project.
Requirements first, then hardware, then software, then bring-up. One clear order, every project.
Robotics Software Engineering
ROS 2 stacks, motion planning, controls, and full system integration.
The software that turns a robot from a bag of motors into a useful product. We architect ROS 2 stacks, write motion-planning code on MoveIt 2, design behavior trees for task orchestration, and integrate sensors, controllers, and grippers into a working cell.
What we do
- ROS 2 architecture, package design, and DDS tuning
- Motion planning with MoveIt 2 (OMPL, CHOMP) and trajectory optimization
- Behavior trees for task-level orchestration (BehaviorTree.CPP)
- Joint, Cartesian, and admittance control with controller_manager
- Integration of vendor SDKs (UR, Franka, myCobot, Robotiq grippers) under one ROS 2 graph
Stack we work with
- ROS 2 (Humble / Jazzy)
- MoveIt 2
- OMPL
- BehaviorTree.CPP
- ros2_control
- Cyclone DDS
Computer Vision & Perception
See, locate, and understand objects in cluttered real-world scenes.
Robots need to know what they are looking at and where it is. We build perception pipelines that combine classical computer vision, deep models, and modern vision foundation models. The output is reliable detections, poses, and grasps that the motion layer can use.
What we do
- 6-DoF object pose estimation with FoundationPose / MegaPose for industrial parts
- Instance segmentation (Mask R-CNN, SAM 2) for clutter and bin-pick scenes
- Visual and visual-inertial SLAM, plus AprilTag-based localization
- RGB-D depth fusion, point-cloud filtering, and grasp-point estimation
- Hand-eye calibration and camera-intrinsics workflows for new cells
Stack we work with
- OpenCV
- Open3D
- PyTorch
- FoundationPose
- SAM 2
- DINOv2
- Depth-Anything
- YOLO
Simulation & Digital Twins
Prove every motion in simulation before you commit to hardware.
Simulation lets us iterate on perception, motion, and policy work without burning a robot. We build high-fidelity Gazebo and Isaac Sim environments, generate synthetic training data, and run Sim2Real bring-up so customer hardware lands on a stack that already works.
What we do
- Gazebo Harmonic worlds and Isaac Sim / Lab environments from your URDF or USD assets
- Synthetic data generation with domain randomization for vision-model training
- Reinforcement-learning training loops (PPO, SAC) and Sim2Real transfer
- Digital-twin views of customer cells for what-if and regression testing
- CI-style nightly policy regression runs across a fixed scenario suite
Stack we work with
- Gazebo Harmonic
- Isaac Sim / Isaac Lab
- MuJoCo
- Stable-Baselines3
- LeRobot
- NVIDIA Replicator
Robotics Hardware
Arm, gripper, sensors, compute, and the bring-up that ties it together.
We help you pick the right collaborative arm, gripper, sensors, and compute for the task. Then we bring the cell up from box-open to working pick-and-place. We focus on commercially available, well-supported parts so the system stays maintainable.
What we do
- Arm selection across cobot tiers (myCobot 280, UR cobots, Franka research arms)
- Gripper selection: parallel-jaw, suction, or a custom 3-finger top-down tool for vial-style tasks
- Sensor selection: RGB-D cameras (RealSense, ZED), wrist F/T, encoders, safety scanners
- Compute and edge selection (Jetson, Intel NUC) for ROS 2 deployment
- Mechanical mounting, power, networking, and operator HMI bring-up
Stack we work with
- Elephant Robotics myCobot 280
- Universal Robots / Franka
- Intel RealSense
- ATI F/T
- NVIDIA Jetson
- Custom mounts and base plates
Want to see this in action?
Read our flagship robotics case study — a simulation-first robotic arm that loads sample vials into HPLC autosampler trays for chemistry labs.
HPLC Autosampler case study