Locomotion & Controls Engineer
Description
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.