Research Scientist, Robot Learning - Continual Adaptation

The Opportunity

We are seeking a creative and technically strong researcher to advance post-training methods for generative robot policies. This role focuses on enabling pretrained robot policies to adapt rapidly from experience and human feedback, improve through autonomous interaction and collaboration, and retain useful capabilities over time.

You will develop algorithms and frameworks for post-training and continual learning in robot foundation models, with the goal of allowing robots to improve after deployment without retraining from scratch.

Responsibilities

• Design and implement methods for adapting generative robot policies using techniques such as reinforcement learning, imitation learning, agentic tool use, and related methods.
• Develop methods that enable generative robot policies to improve through ongoing interaction with people and the environment, using signals such as demonstrations, interventions, preference feedback, learned rewards, and other human-driven signals.
• Develop rollout-based learning and exploration strategies that enable robots to efficiently and safely gather useful experience, leveraging multimodal data from demonstrations, teleoperation, interventions, and on-policy rollouts, in both simulation and on real robot hardware.
• Collaborate across teams in perception, control, foundation models, and ML infrastructure to deploy scalable and reproducible research systems.
• Publish research outcomes and contribute to the open robotics and embodied AI communities.

Qualifications

• Ph.D. or M.S. in Robotics, Machine Learning, Computer Vision, or a related field, or equivalent applied research experience.
• Strong background in reinforcement learning, imitation learning, or closely related robot-learning methods.
• Strong proficiency with deep learning frameworks such as PyTorch or JAX and robotics simulation environments such as IsaacLab or MuJoCo.
• Experience with policy adaptation, continual learning, preference learning, or related learning problems in embodied settings.
• Strong coding and experimental skills with an emphasis on reproducibility and rigorous evaluation.
• Prior robotics experience with real-world hardware and ML-based robot deployments.
• Publications in top-tier conferences such as CoRL, RSS, NeurIPS, ICLR, ICML, ICRA, or CVPR.

Bonus Qualifications

• Experience with human-in-the-loop robot learning (e.g., DAgger), preference learning, reward learning, or RLHF/RLAIF.
• Prior work on generative robot policies or VLAs such as π0.5, GR00T, or related models.
• Experience building or managing robot data collection or replay infrastructure.
• Familiarity with real-world robot platforms such as YAMs, Kinova, humanoids, or mobile manipulators.