Robotics Intern, Post-Training and Continual Learning

The Team

Our team in the Robotics division is developing pretrained generalist policies that can support a broad range of tasks, environments, and robotic systems. A central scientific challenge is determining how these broadly pretrained policies can be efficiently adapted to new target tasks while achieving the reliability, precision, and robustness required for real-world use.

Pretraining can provide a policy with broad behavioral capabilities, but those capabilities may not immediately translate into dependable performance in a specific deployment setting. Effective post-training methods are therefore essential for converting general capabilities into policies that can adapt quickly, learn from limited additional data or interaction, and perform consistently on demanding downstream tasks.

The Opportunity

Our research interests include reinforcement learning, imitation learning, human-in-the-loop learning, simulation, world models, policy distillation, and large-scale robot learning. We aim to advance the scientific foundations of policy adaptation while developing methods that may ultimately be evaluated or deployed within internal research projects involving real-world industrial tasks.

Potential research directions include, but are not limited to:

• 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

Responsibilities

• Develop post-training methods that adapt pretrained generalist robot policies to new tasks, environments, and robotic systems.
• Design and conduct rigorous experiments in simulation and, where appropriate, on real robotic systems.
• Analyze results, communicate findings, and contribute to research publications and internal technical reports.

Qualifications

• 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.

Bonus Qualifications

• 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.