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Ghost Robotics Tech Stack

Quadrupedal robots for military, industrial inspection, and harsh-terrain operations

Industrial Machinery Manufacturing Philadelphia, PA 51–200 employees Founded 2015 Privately Held

Ghost Robotics designs four-legged autonomous robots (Q-UGVs) engineered for unstructured and extreme terrain where wheeled and tracked platforms fail. The tech stack reveals a hardware-software co-design org: embedded control (STM32, FreeRTOS, EtherCAT) paired with simulation (Isaac Sim, Gazebo, MuJoCo) and perception (ORB-SLAM, Cartographer, GTSAM). Active projects span reinforcement learning for self-righting, firmware for embedded subsystems, SLAM/localization, and manipulation algorithms—indicating a maturity shift from basic locomotion toward autonomous behaviors and payload integration. Pain points highlight the gap between research prototypes and field-hardened production: reliability, real-world performance variability, ISO 9001 certification, and DOD procurement complexity.

Tech Stack 29 technologies

Core StackPython Weights & Biases C++ QuickBooks Isaac Sim C/C++ Altium STM32 FreeRTOS EtherCAT QSPI Adobe MuJoCo Gazebo PyBullet ROS 2 ROS CUDA OpenCL ORB-SLAM Cartographer GTSAM MoveIt Altium Designer NVIDIA Jetson RAMP FloQast Paylocity Odoo

What Ghost Robotics Is Building

Challenges

  • Robot reliability
  • Field performance
  • Complex real-world environments
  • Bridging gap between research and production
  • Building quality function from scratch
  • Achieving iso 9001 certification
  • Supplier quality governance
  • Expanding market share in defense sector
  • Securing government contracts
  • Navigating dod procurement

Active Projects

  • Reinforcement learning for quadrupedal robot self-righting
  • Firmware development for embedded robotic subsystems
  • Payload deployment kit deployment
  • Advanced control architectures
  • State estimation pipelines
  • Automated diagnostics
  • Slam and localization system development
  • Payload communication integration
  • Perception system performance evaluation
  • Manipulation algorithms for dynamic legged robots

Hiring Activity

Accelerating10 roles · 6 in 30d

Department

Engineering
8
Executive
1
Finance
1

Seniority

Senior
6
Intern
1
Junior
1
Mid
1
VP
1
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About Ghost Robotics

Ghost Robotics develops legged autonomous ground vehicles (Q-UGVs) for military, industrial, mining, energy, and public safety markets. The product line spans the VISION series for civilian inspection, asset management, and scientific applications across manufacturing and infrastructure, and the WRAITH series for specialized military operations. Founded in 2015 and based in Philadelphia, the company operates a 51–200 person engineering-led organization. The technical approach prioritizes locomotion over unstructured terrain and sensor integration; projects focus on control architectures, state estimation, localization, and autonomous manipulation to expand the robot's operational scope.

HeadquartersPhiladelphia, PA
Company Size51–200 employees
Founded2015
Hiring MarketsUnited States

Frequently Asked Questions

What tech stack does Ghost Robotics use?

Embedded: Python, C/C++, STM32, FreeRTOS, EtherCAT. Simulation & control: Isaac Sim, Gazebo, MuJoCo, PyBullet, ROS 2, MoveIt, CUDA. Perception: ORB-SLAM, Cartographer, GTSAM. Hardware design: Altium Designer. Backend: NVIDIA Jetson.

What is Ghost Robotics working on?

Active projects include reinforcement learning for self-righting, firmware development for embedded subsystems, SLAM/localization system development, state estimation pipelines, advanced control architectures, payload deployment integration, and manipulation algorithms for dynamic legged locomotion.

How this profile is built

Ghost Robotics's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →

This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.