Research Scientist, Robot Learning - Continual Adaptation

Toyota Research Institute is seeking a researcher to advance post-training and continual learning methods for generative robot policies. The role develops algorithms that help pretrained robot policies adapt through experience and human feedback, improve through interaction, and retain capabilities after deployment.

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