Research Associate (Robotics)
Core
Develop robust, intelligent navigation systems for inspection and humanoid robots, focusing on autonomous localization, decision-making, motion planning, and safe navigation in complex, dynamic environments.
Role type
Research Associate (Robotics Navigation & Learning)
Builds
Navigation algorithms, motion planning systems, and learning-based control policies for humanoid robots
Domain
Robotics, Autonomous Systems, Machine Learning
Deliverable
production ML models | product features
Required skills
robot motion planning and control, autonomous decision-making, robot localization and navigation, path planning, trajectory optimization, reinforcement learning, imitation learning, diffusion policies, robotics physics simulators, Python, C++, PyTorch
Preferred skills
generative decision-making, learning-based control, humanoid locomotion, whole-body control
Technologies
Isaac Gym, MuJoCo, Gazebo, PyTorch
Responsibilities
Design and develop navigation algorithms for inspection robots; Work on global and local motion planning, obstacle avoidance, and trajectory tracking; Investigate diffusion models and diffusion-based policies for navigation planning; Apply reinforcement learning and/or imitation learning methods; Integrate visual navigation modules with humanoid locomotion and whole-body control systems; Build and evaluate algorithms in simulation and on real-world humanoid robotic platforms; Contribute to publications and technical reports; Assist in mentoring students and supporting research activities
