Robot Control Researcher – Advanced Robotics

As a Robot Control Researcher, your purpose will be to research, develop, and deploy cutting-edge control algorithms for legged (biped/quadruped) and contact-rich robotic systems! Be part of a multidisciplinary culture with a diverse mindset! As the largest robotics company based in India, Addverb offers the opportunity to work alongside brilliant minds and be part of a collaborative, dynamic culture. With international exposure and a flexible work environment based on freedom with responsibility, Addverb offers endless opportunities for growth and learning.

Role

The purpose of this role is to research, develop, and deploy cutting-edge control algorithms for legged (biped / quadruped) and contact-rich robotic systems. The Robot Control Researcher will work at the intersection of optimal control theory, reinforcement learning, machine learning and real-world deployment, driving the development of robots capable of dynamic, adaptive, and intelligent behaviour in complex environments.

  • EMEA
  • Advanced Robotics
  • Full-Time Role

Responsibilities

 
    • Design, implement, and test advanced control algorithms for dynamic robotic systems, including manipulators, quadrupeds, and humanoids.
    • Conduct applied research in areas such as trajectory optimization, motion planning, adaptive control, and reinforcement learning.
    • Develop simulation environments and validate control strategies in both virtual and physical platforms. Experience in Mujoco / Isaac Platforms
    • Collaborate with hardware engineers, software developers, and other researchers to integrate control systems into robotic platforms.
    • Publish research findings in peer-reviewed journals and top-tier robotics conferences.
    • Continuously stay informed of cutting-edge advancements in robotics, AI, and control systems, and translate new research into actionable improvements.

Key Skills, Qualifications, and Required Years of Experience

 
  • Strong background in control theory, dynamics, and kinematics of robotic systems.
  • Proficiency in programming languages such as Python, C++, or MATLAB.
  • Experience with robotics middleware (e.g., ROS/ROS2) and simulation tools (e.g., Gazebo, MuJoCo, PyBullet).
  • Solid understanding of optimization techniques and machine learning methods applied to control.
  • Experience with real-time control systems and embedded programming.
  • Familiarity with hardware platforms such as robotic arms, drones, or quadrupeds.
  • Track record of publications in relevant academic venues.
  • Experience with reinforcement learning or model predictive control (MPC).
  • PHD or Master’s degree in Robotics, Electrical Engineering, Mechanical Engineering, Computer Science, or a related field (either pursuing or recently completed)
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