Locomotion & Controls Engineer

JD Locomotion & Controls Engineer About Innefu Labs Founded in 2010, Innefu Labs is an AI-driven R&D organisation focused on advanced Data Analytics and Information Security solutions. With a strong emphasis on innovation and product engineering, the company develops globally competitive solutions addressing complex challenges in national security, cyber intelligence, and data analytics. With 100+ successful deployments across the Indian Subcontinent, the Middle East, and Southeast Asia, Innefu has established a strong presence in AI for National and Cyber Security. Its proprietary products serve Defence and Intelligence agencies, Law Enforcement organisations, Financial Intelligence Units, BFSI institutions, and Fortune 500 companies. Innefu specialises in building secure, high-performance products for sensitive and mission-critical environments, with a strong focus on innovation, reliability, scalability, and customer satisfaction

Role Overview: Makes the robot walk, climb and recover on mud, rock, snow and stairs - designing and training the gait controller in simulation and transferring it to the real robot. Experience: 3-8 years,

Education: MTech/PhD with a thesis on legged locomotion. BTech/MTech/PhD in Mechanical, Electrical, Aerospace, Robotics or Control Systems. Very few people in India have built legged robots, so a strong humanoid, exoskeleton or legged-research background also counts.

Responsibilities

• Build the simulation environment with randomised rough terrain.
• Develop gaits (walk, trot, stairs, crawl) using reinforcement learning, model predictive control (MPC), or both.
• Transfer controllers from simulation to the real robot (sim-to-real) and tune them in field tests.
• Write state estimation (body pose and velocity) from IMU and joint sensors.
• Specify actuator and leg requirements to choose the off-the-shelf supplier. Must have
• C++ and Python.
• Rigid-body dynamics and control theory: MPC, whole-body control, or RL for locomotion (PPO).
• At least one physics simulator: Isaac Sim / Isaac Lab, MuJoCo, PyBullet, Gazebo or RaiSim.
• Has run a controller on real robot hardware, not only in simulation. Good to have
• Hands-on with commercial quadrupeds (Unitree, ANYmal, Spot, Deep Robotics) or their SDKs.
• Open-source legged stacks: legged_gym, OCS2, Pinocchio, Drake, Crocoddyl.
• Publications at ICRA, IROS, CoRL, RSS or Humanoids.
• Kalman filters / state estimation; terrain-aware foothold planning.
• PyTorch, GPU training, domain randomisation.

Requirements

Responsibilities

• Build the simulation environment with randomised rough terrain.
• Develop gaits (walk, trot, stairs, crawl) using reinforcement learning, model predictive control (MPC), or both.
• Transfer controllers from simulation to the real robot (sim-to-real) and tune them in field tests.
• Write state estimation (body pose and velocity) from IMU and joint sensors.
• Specify actuator and leg requirements to choose the off-the-shelf supplier. Must have
• C++ and Python.
• Rigid-body dynamics and control theory: MPC, whole-body control, or RL for locomotion (PPO).
• At least one physics simulator: Isaac Sim / Isaac Lab, MuJoCo, PyBullet, Gazebo or RaiSim.
• Has run a controller on real robot hardware, not only in simulation. Good to have
• Hands-on with commercial quadrupeds (Unitree, ANYmal, Spot, Deep Robotics) or their SDKs.
• Open-source legged stacks: legged_gym, OCS2, Pinocchio, Drake, Crocoddyl.
• Publications at ICRA, IROS, CoRL, RSS or Humanoids.
• Kalman filters / state estimation; terrain-aware foothold planning.

• PyTorch, GPU training, domain randomisation.