Robotics Research Internship, Humanoid Manipulation (Summer 2027) | PhD Internship

FieldAI is transforming how robots interact with the real world. Our growing R&D team is based in Boston, where we develop risk-aware, reliable, field-ready AI systems that tackle the hardest problems in robotics and unlock the potential of embodied intelligence. We take a pragmatic approach that goes beyond off-the-shelf, purely data-driven methods or transformer-only architectures, combining cutting-edge research with real-world deployment. Our solutions are already deployed globally, and we continuously improve model performance through rapid iteration driven by real field use.

We are offering a Fall 2026 internship focused on humanoid manipulation for PhD students interested in advancing embodied intelligence on humanoids. As a research intern, you will work at the intersection of robotics research and applied engineering, contributing to manipulation capabilities that directly support FieldAI’s autonomy and robot learning efforts while advancing the field of robotics through academic research with the goal of publishing a paper to a leading journal or conference.

Advance Humanoid Manipulation Research Design, implement, and evaluate manipulation strategies for humanoid robots across diverse tasks. Explore loco-manipulation problems that integrate perception, planning, and control. Work towards super-human dexterity, developing systems that outperform human teleoperation. Contribute to the Research Community Work towards publishing a paper in a leading robotics conference/journal Work closely with other researchers, both internal and external. Present your work at Field at robotic conferences Contribute to Robotics Foundation Model Development Support data collection pipelines used to train robotics foundation models. Work with embodiment-agnostic representations to enable transfer across robot platforms. Collaborate with researchers to integrate manipulation data into scalable learning frameworks. Collaborate Across Disciplines Work with teleoperators and field teams to refine interfaces and improve manipulation outcomes. Partner closely with researchers and engineers to align experiments with broader autonomy goals. Engage with mechanical and electrical engineers on hardware integration and system bring-up.

Current PhD student in Robotics, Computer Science, Mechanical Engineering, Electrical Engineering, AI/ML, or a closely related field. Research experience in robotic manipulation, loco-manipulation, or related robotics domains. Strong foundation in robot kinematics, dynamics, and control. Proficiency in Python and/or C++, with experience using robotics or ML tooling. Experience designing experiments and evaluating results on robotic systems (simulation or hardware). Curiosity, initiative, and a strong interest in embodied intelligence and real-world robotics. Publications in leading robotics journals/conferences (ICRA, IROS, CORL, RA-L, T-RO, etc.)

Prior experience working with humanoid robots or dexterous robotic hands. Background in learning-based manipulation, including imitation learning or reinforcement learning. Hands-on experience running experiments on real robot hardware. Familiarity with ROS or ROS 2. Publications, preprints, or open-source contributions in robotics or AI. Interest in bridging cutting-edge research with practical, field-ready robotic systems.

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