About this Robotics Research Intern, Manipulation role at Tri
At Toyota Research Institute (TRI), we’re on a mission to improve the quality of human life. We’re developing new tools and capabilities to amplify the human experience. To lead this transformative shift in mobility, we’ve built a world-class team advancing the state of the art in AI, robotics, driving, and material sciences.
This is a paid 12-week internship opportunity and is a hybrid, in-office role.
Here’s a glimpse into the Internship experience from some of our TRI interns!
The Team/Project
TRI's Robotics Division develops technologies that enable robots to operate effectively in complex, real-world environments. The project explores how foundation-model agents can understand manipulation tasks, reason about physical environments, and orchestrate reusable robotic capabilities to accomplish diverse objectives. Our research combines vision-language models, robotic perception, motion planning, manipulation, and robot learning. We investigate these questions through hands-on experimentation on physical robotic platforms.
The Challenge
Recent advances in foundation models have significantly improved AI systems' ability to understand visual scenes, reason about tasks, and plan sequences of actions. However, translating these capabilities into reliable physical robot behavior remains challenging. Robots must reason about three-dimensional geometry, physical constraints, contact interactions, and failures during execution.
An important research question is how far foundation-model agents can go by leveraging existing robotic capabilities, and when specialized learned policies become necessary.
The Opportunity
We are seeking a Robotics Research Intern to investigate agentic approaches to robotic manipulation. You will work closely with researchers and engineers to develop systems that connect foundation-model reasoning with physical robot capabilities.
Depending on your background and research interests, your project may focus on spatial reasoning, robotic skill orchestration, execution monitoring, failure recovery, or systematic evaluation of agentic manipulation systems.
This internship offers an opportunity to tackle open research questions while gaining practical experience with cutting-edge AI models and real robotic systems. Your work will contribute to understanding how foundation-model agents can enable more generalizable and reliable robot autonomy.
Responsibilities
Develop and evaluate agentic approaches that use vision-language models to reason about and execute robotic manipulation tasks.
Integrate foundation-model agents with reusable robotic capabilities, including perception, motion planning, grasping, and manipulation.
Investigate challenges in three-dimensional spatial reasoning, robot action feasibility, execution monitoring, and failure recovery.
Design and conduct experiments on physical robotic platforms or in simulation.
Analyze system performance, characterize failure modes, and evaluate generalization across tasks and environments.
Communicate research findings through technical presentations, demonstrations, reports, and potential publications.
Qualifications
Currently pursuing a Master's or PhD degree in Robotics, Computer Science, Electrical Engineering, Mechanical Engineering, or a related field.
Strong programming skills in C++ and/or Python and experience developing, testing, or debugging technical software.
Solid understanding of robotics fundamentals, including one or more of robot kinematics, dynamics, motion planning, perception, or control.
Experience with robotics-related projects through research, coursework, or personal projects, using physical hardware and/or simulation.
Ability to formulate technical problems, design experiments, and systematically analyze results.
Interest in robotic manipulation, autonomous systems, and connecting high-level reasoning with physical robot capabilities.
Bonus Qualifications
Hands-on experience with robotic manipulators, motion planning frameworks, or robot simulation environments.
Familiarity with 3D geometry, computer vision, grasp planning, or contact-rich manipulation.
Experience with ROS/ROS 2, or robotics software frameworks.
Experience with vision-language models, LLM agents, or autonomous planning systems.
Familiarity with robot learning methods such as imitation learning, reinforcement learning, or vision-language-action models.
Research experience demonstrated through publications, open-source contributions, or substantial technical projects.
