Research Scientist Intern, Dexterous Manipulation and Contact-Rich Control (PhD)
About The Position
Agios is a full-stack research organization within Reality Labs Research, spanning materials and manufacturing, sensors and actuators, simulation, and learned control. Its mission is to build systems that enhance human capabilities through intelligent machine collaboration. The Dexterous Manipulation group works to address the gap between advances in digital research and development and the slower pace of physical work, using a custom dexterous robotic hand, in-house wearable sensing, high-accuracy simulation, and glove-driven teleoperation. The Research Scientist Intern will work on fast, stable learned feedback control for multi-fingered robotic hands. The work focuses on reinforcement learning in high-fidelity simulation, tactile and proprioceptive feedback, and transferring learned policies to real robot hands, with mentorship across hardware, simulation, controls, and policy learning. Internships last 12 to 24 weeks, with various start dates throughout the year; the preferred qualifications also specify availability for a minimum of 16 consecutive weeks.
Requirements
- Research experience in reinforcement learning or learning-based control for robotic manipulation or locomotion, demonstrated through publications, open-source code, or substantial projects.
- Experience with Python and a physics simulator such as MuJoCo, Isaac, or Drake.
- Must obtain work authorization in the country of employment by the time of hire and maintain it throughout employment.
Nice To Haves
- A record of significant results demonstrated through grants, fellowships, patents, or first-authored publications at leading workshops or conferences such as RSS, CoRL, ICRA, IROS, T-RO, IJRR, NeurIPS, ICML, or ICLR.
- Experience with sim-to-real transfer of contact-rich, locomotion, or dexterous manipulation policies, including domain randomization, system identification, and teacher–student distillation.
- Experience using tactile sensing, force/torque sensing, or contact estimation in policy learning or control.
- Experience in retargeting for locomotion or manipulation.
- Grounding in manipulation mechanics and control, such as contact modeling, impedance and admittance control, and trajectory optimization.
- Hands-on experience with multi-fingered robotic hands or grippers on real hardware.
- Experience working and communicating cross-functionally in a team environment.
- Availability for a minimum 16 consecutive week internship.
Responsibilities
- Drive an independent research thread on learned reactive control for dexterous hands, from problem formulation through simulation studies and real-robot evaluation.
- Design controlled experiments and benchmarks that isolate issues with manipulation dexterity.
- Integrate proprioceptive signals into control policies and close the sim-to-real gap for contact through system identification, domain randomization, and realistic models of upstream policy error.
- Collaborate across hardware, simulation, controls, and policy research, and communicate research plans, progress, and results.
Benefits
- Benefits are offered; specific benefits are not listed.
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What This Job Offers
Job Type
Full-time
Career Level
Intern
Education Level
No Education Listed
