Tasks:
- * Apply system identification, state estimation, and domain randomization
- * Build parallel robot simulation training environments
- * Deploy motion-control systems on edge hardware
- * Develop force-control strategies for varied terrain
- * Develop reinforcement and imitation learning locomotion algorithms
- * Implement trajectory tracking and whole-body control
- * Improve sim-to-real transfer and deployment robustness
- * Integrate perception, decision-making, and motion execution
Perks/Benefits:
- + Encouraged to publish conference papers
- + End-to-end simulation-to-real deployment experience
- + Mentorship from experienced algorithm leads
- + Opportunity to lead core module design
Skills/Tech stack required:
[Body Control] [C++] [Domain Randomization] [Force control] [Forward and inverse kinematics] [Gazebo] [Imitation Learning] [Inverse kinematics] [Isaac-Gym] [Isaac Sim] [Motion Control] [Mujoco] [Multibody dynamics] [PPO] [PyBullet] [Python] [PyTorch] [Reinforcement Learning] [Robot dynamics] [Robot Kinematics] [Robot motion] [Robot Motion Control] [SAC] [Sim-to-Real] [Sim-to-Real Transfer] [State Estimation] [System Identification] [Trajectory tracking] [Whole-body control]
Educational requirements:
[Master's Degree]
Role(s):
[Algorithm Engineer] [Control Algorithm Engineer] [Control Engineer] [Engineer] [Learning Engineer] [Motion Control Algorithm Engineer] [Reinforcement Learning Engineer] [Robotics & Control Engineer]