Motion Planning Jobs for Robotics and Path Planning Engineers

Key Takeaways

• Motion planning engineers design the algorithms that calculate how a robot moves from one place to another safely, efficiently, and within its physical limits.
• The role sits between autonomy (what to do) and controls (how to execute), making it one of the most technically cross-disciplinary specializations in Physical AI;
• Mid-level motion planning engineers in the US earn between $130k and $180k, with senior roles at humanoid and AV companies reaching $225k.
• Most engineering-track roles do not require a PhD; strong candidates with optimization backgrounds and MoveIt or OMPL experience are competitive.

Motion planning is the research area which addresses the question of how to get the robot from point A to point B. Just telling a robot arm to grab an object is not sufficient; there must be an algorithm which computes a valid trajectory for the joints, taking into account obstacles in the surroundings, respecting the limitations of speed and acceleration of the robot and computing it fast enough to perform in real time.

What Is Motion Planning?

Motion planning entails the determination of a series of acceptable positions and motions for a robot that will take it from its initial position to its final one without violating constraints. Constraints might be things such as presence of obstacles, maximum allowable joint angles, maximum allowable speeds, torque limitations, as well as task requirements like holding a cup steady when carrying it somewhere.

Motion planning is used in various robotic applications that require motion of a physical object in space, such as manipulation, mobile robots navigating factory floors, self-driving cars switching lanes, or humanoid robots walking in a cluttered environment.

Motion Planning vs Path Planning: What Is the Difference?

Path planning is included in motion planning. Path planning is concerned with the finding of a path, while motion planning involves the discovery of a trajectory which is feasible for the robot. The term "motion planning" is more often employed in job advertisements in robotics.

Manipulation planning for robot arms is the fastest-growing motion planning sub-specialty on our platform, driven by the expansion of humanoid robot programs & advanced assembly automation; Demand for engineers who can work in high-dimensional joint spaces, handle contact-rich tasks, and integrate learned components alongside classical planners has grown significantly in the past 12 months. - Physical AI Jobs

MoveIt is the standard open-source motion planning framework for robot arms in ROS 2 environments and is expected knowledge for most manipulation planning roles. OMPL (Open Motion Planning Library) is the underlying planning library used by MoveIt and many other production planning systems, and understanding its sampling-based algorithms is a genuine differentiator for candidates.

For a full skills breakdown across Physical AI disciplines, see our guide on what skills you need for robotics and Physical AI careers.

Trajectory Optimization Systems: The Core Technical Work

The trajectory optimization technique involves mathematics the most among the techniques used in motion planning. Rather than selecting random positions and finding optimal paths between them, it approaches the task of path finding from a mathematical perspective and solves it by minimizing a cost function while satisfying constraints such as collision avoidance and dynamics constraints.

Key methods used in production systems:

• CHOMP (Covariant Hamiltonian Optimization for Motion Planning): Gradient-based optimization that produces smooth, collision-free trajectories for robot arms.
• TrajOpt: Sequential convex optimization approach, widely used for manipulation planning where contact and constraints are complex;
• iLQR (Iterative Linear Quadratic Regulator): Optimal control method used extensively in legged locomotion and dynamic manipulation.
• MPC (Model Predictive Control): Online optimization that re-plans a trajectory at each timestep, handling dynamic environments & moving obstacles.

The line between motion planning and controls is blurring as MPC-based approaches become standard in both domains. Motion planning engineers at advanced robotics companies increasingly need controls fluency, and vice versa.

Our analysis of motion planning job postings shows that 61% now list trajectory optimization as a required skill, up from 43% 2 years ago. This reflects the industry's move away from purely sampling-based methods toward optimization-based approaches that produce smoother, more dynamically feasible trajectories for complex robot systems. - Physical AI Jobs

Robot Motion Control Careers: Adjacent and Related Roles

Motion planning connects closely to several other Physical AI specializations. Engineers from motion planning backgrounds frequently work alongside or transition into:

• Controls engineering: Controls executes the trajectories that planning generates. MPC creates significant overlap between the two disciplines.
• Autonomy engineering: Autonomy decides the goals and high-level strategy; motion planning translates them into executable trajectories;
• SLAM engineering: Planning uses the map SLAM produces to compute collision-free paths in real environments.
• Simulation engineering: Motion planning algorithms are heavily tested and developed in simulation before hardware deployment.

For engineers building toward a motion planning role, our guide on how to build a robotics portfolio explains how to structure manipulation and planning projects for maximum credibility.