Limbed Robotics: Dynamic Whole-Body Mobility and Manipulation

About the role

The position involves research on dynamic whole-body mobility and manipulation capabilities of autonomous limbed robots. The work focuses on developing control authorities and understanding the dynamics governing the robotic system using physics-based, multi-bodied models. Techniques for behavior development include model-based approaches, machine learning, and hybrid methods. The research is conducted across various platforms, including wheeled, aerial, quadrupedal, and bipedal systems, with or without manipulators. Additionally, the role may explore unique forms of mobility and manipulation.

Responsibilities

• Develop and validate physics-based, multi-bodied models for robotic systems.
• Design and implement control algorithms for dynamic mobility and manipulation.
• Collaborate with simulation and physical platform teams to iterate and improve performance.
• Contribute to the advancement of autonomous behaviors for agile traversal and manipulation in complex environments.
• Participate in the design and execution of experiments to validate simulation results.

Requirements

• Bachelor's, Master's, or Doctoral Degree in Computer, Information, and Data Sciences, Engineering, Mathematics and Statistics, Physics, Science & Engineering-related, Chemistry and Materials Sciences, Communications and Graphics Design, or related field.
• At least 18 years of age.
• Strong background in robotics, control theory, and computational methods.
• Experience with machine learning, simulation, and experimental validation.
• Ability to work independently and collaboratively in a multidisciplinary team environment.

Qualifications

• Proficiency in programming languages such as Python, MATLAB, or C++.
• Knowledge of robotics frameworks like ROS, Gazebo, or Mujoco.
• Experience with data analysis and statistical methods.
• Understanding of control theory and dynamical systems.
• Excellent problem-solving skills and attention to detail.

Skills

• Robotic systems design and control.
• Simulation and experimental validation techniques.
• Machine learning and data-driven approaches.
• Physics-based modeling and optimization.
• Team collaboration and project management.

Benefits

• Opportunities for mentorship and collaboration with leading researchers.
• Access to state-of-the-art facilities and equipment.
• Competitive salary and benefits package.
• Flexible working arrangements and remote work options.

Pay

• Salary range based on experience and qualifications.

Schedule

• Full-time position with flexible hours.

Contact

For questions about this opportunity, please email ARLFellowship@orau.org.