Robotics Research Intern - Post-Training

We are looking for two Research Interns to investigate open research questions in the post-training and adaptation of pretrained generalist robot policies. Potential research directions include: Offline-to-online reinforcement learning; DAgger, imitation learning, and human-in-the-loop policy improvement; Sim-to-real policy distillation and adaptation; Policy improvement, planning, or data generation using world models; Data-efficient adaptation to new tasks and environments. Internship projects will be scoped according to each intern’s research background, interests, and current team priorities. Interns will work closely with researchers and engineers across the Robotics division, with the goal of producing meaningful scientific results and, where appropriate, publications at leading robotics or machine-learning venues.

Responsibilities

Investigate open research questions in the post-training and adaptation of pretrained generalist robot policies; Work closely with researchers and engineers across the Robotics division; Produce meaningful scientific results and, where appropriate, publications at leading robotics or machine-learning venues

Requirements

Currently pursuing a Ph.D. in Computer Science, Machine Learning, Robotics, or a related field; Research experience in robot learning, reinforcement learning, imitation learning, generative modeling, world models, or a related area; Interest in open research problems involving large-scale machine learning grounded in physical systems; Proficiency in Python and a deep-learning framework such as PyTorch; Ability to collaborate effectively with researchers and engineers and communicate research findings clearly

Nice to have

Experience with pretrained generalist policies, foundation models, or large-scale robot-learning systems; Familiarity with offline or online reinforcement learning, DAgger, interactive learning, or human-in-the-loop methods; Experience with simulation, sim-to-real transfer, policy distillation, or robotic manipulation; Experience with learned world models, model-based reinforcement learning, or planning; Publication record or interest in publishing at leading venues such as CoRL, NeurIPS, ICLR, ICML, RSS, ICRA, IROS, or related conferences and journals; Interest in translating fundamental research into reliable methods that can be evaluated on real robotic systems and practical downstream tasks

Benefits

Medical, dental, and vision insurance; Paid time off benefits including holiday pay and sick time