Research Scientist Intern, Dexterous Manipulation and Contact-Rich Control (PhD)
Meta
Research Scientist Intern, Dexterous Manipulation and Contact-Rich Control (PhD)
Meta
Posted 11 days ago
Internship
Redmond, Washington
In Person
Smart Summary
Responsibilities
The intern will drive an independent research thread on learned reactive control for dexterous hands, covering problem formulation through real-robot evaluation. They will also collaborate with an interdisciplinary team to integrate proprioceptive signals and close the sim-to-real gap for contact-rich manipulation.
Qualifications
You have research experience in reinforcement learning or learning-based control for robotic manipulation or locomotion, demonstrated through publications, open-source code, or substantial projects. You are proficient in Python and have experience with physics simulators like MuJoCo, Isaac, or Drake.
Must Have Skills for ATS
reinforcement learning
learning-based control
robotic manipulation
Python
MuJoCo
Isaac
Drake
sim-to-real transfer
system identification
domain randomization
tactile sensing
force/torque sensing
contact estimation
manipulation mechanics
control
impedance control
admittance control
trajectory optimization
Job Description
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 an interdisciplinary team of hardware, simulation, controls, and policy researchers, and communicate research plans, progress, and results.
Minimum Qualifications
- 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 with a physics simulator such as MuJoCo, Isaac, or Drake
- Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment
Preferred Qualifications
- Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as 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 minimum 16 consecutive week internship
$7,650/month to $12,667/month + benefits
Meta
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