Research Scientist Intern, Robot Foundation Models (PhD)

Agios is a full stack research organization within Reality Labs Research, spanning materials and manufacturing, sensors and actuators, simulation, and learned control. Our mission is to build systems that enhance human capabilities through intelligent machine collaboration. Today that work centers on what we call the last bottleneck: AI has compressed the digital side of research and development, while the physical side still moves at human speed. Dexterous Manipulation is the pillar attacking that problem directly, pairing a custom dexterous robotic hand with in-house wearable sensing, high accuracy simulation, and glove-driven teleoperation.

As a Research Scientist Intern, you will work at the intersection of robot learning, dexterous control, and human-to-robot transfer, with mentors who span hardware, simulation, controls, and policy learning. Your focus will be learned models that turn human demonstration data into executable robot behavior, covering retargeting, manipulation policies, and teleoperation.

Our internships are twelve (12) to twenty-four (24) weeks long and we have various start dates throughout the year.

Responsibilities

• Drive an independent research thread on learned control and motion transfer for dexterous hands, from problem formulation through real robot or high fidelity simulation evaluation.
• Build training pipelines for modern policy classes, including flow matching and diffusion policies, imitation learning, reinforcement learning, large behavior models, and VLAs.
• Develop representations that fuse multimodal human and robot data, including video, hand pose, tactile and contact force, and IMU, into a shared action space.
• Construct data collection pipelines and benchmarks for dexterous manipulation, including held-out and out-of-distribution protocols that test generalization across objects, motions, and embodiments.
• Collaborate across an interdisciplinary team of hardware, simulation, controls, and policy researchers, and communicate research plans, progress, and results.

Minimum qualifications

• Currently has or is in the process of obtaining a Ph.D. degree in Computer Science, Artificial Intelligence, Robotics, or relevant technical field
• Understanding of the state of the art in learned dexterous manipulation and control, with judgment about which open problems are worth advancing
• Experience with Python and PyTorch or JAX, plus familiarity with ROS or comparable robotics middleware
• Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment 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 Robotics (RSS, ICRA, IROS, CoRL, T-RO, IJRR), Machine Learning (NeurIPS, ICML, ICLR, AAAI, JMLR), and Computer Vision (CVPR, ICCV, ECCV, TPAMI), or similar
• Experience training modern control policies, including flow matching, diffusion, imitation learning, reinforcement learning, and large behavior models
• Familiarity with modeling and analysis used in robotics including kinematics, dynamics, trajectory optimization, motion planning, perception, task planning, and control theory
• Hands-on experience with multi-fingered robotic hand manipulation, and with parallel-jaw or other grippers mounted on a robot arm
• Experience with motion retargeting across human and robot embodiments
• Experience with sim-to-real and real-to-sim workflows, including domain randomization, system identification, scene reconstruction, and closing the transfer gap
• Experience collecting and learning from real robot hand demonstrations, whether through teleoperation, kinesthetic guidance, or wearable capture, with bimanual setups a plus
• Experience with multimodal or self-supervised representation learning and cross-embodiment transfer
• Experience with vision based input recognition systems, such as hand tracking, pose estimation, and object pose estimation
• Experience working with time sequence sensor data, such as tactile and IMU streams
• Experience with hand force estimation and its applications in robot policy learning
• Experience designing data collection protocols and building high quality, diverse datasets
• Experience bringing up and debugging prototype software and hardware systems such as robot platforms, multi-camera tracking rigs, or wearable sensing systems
• Experience working and communicating cross functionally in a team environment
• Intent to return to the degree program after the completion of the internship/co-op
• Availability for minimum 16 consecutive week internship

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 Robotics (RSS, ICRA, IROS, CoRL, T-RO, IJRR), Machine Learning (NeurIPS, ICML, ICLR, AAAI, JMLR), and Computer Vision (CVPR, ICCV, ECCV, TPAMI), or similar
• Experience training modern control policies, including flow matching, diffusion, imitation learning, reinforcement learning, and large behavior models
• Familiarity with modeling and analysis used in robotics including kinematics, dynamics, trajectory optimization, motion planning, perception, task planning, and control theory
• Hands-on experience with multi-fingered robotic hand manipulation, and with parallel-jaw or other grippers mounted on a robot arm
• Experience with motion retargeting across human and robot embodiments
• Experience with sim-to-real and real-to-sim workflows, including domain randomization, system identification, scene reconstruction, and closing the transfer gap
• Experience collecting and learning from real robot hand demonstrations, whether through teleoperation, kinesthetic guidance, or wearable capture, with bimanual setups a plus
• Experience with multimodal or self-supervised representation learning and cross-embodiment transfer
• Experience with vision based input recognition systems, such as hand tracking, pose estimation, and object pose estimation
• Experience working with time sequence sensor data, such as tactile and IMU streams
• Experience with hand force estimation and its applications in robot policy learning
• Experience designing data collection protocols and building high quality, diverse datasets
• Experience bringing up and debugging prototype software and hardware systems such as robot platforms, multi-camera tracking rigs, or wearable sensing systems
• Experience working and communicating cross functionally in a team environment
• Intent to return to the degree program after the completion of the internship/co-op
• Availability for minimum 16 consecutive week internship