- Research, ideate, and implement novel and robust model- and learning-based robot behaviors and interaction control algorithms, enabling contact-rich manipulation in various real-world application domains including dexterous object manipulation.
- Research, ideate, and implement robot reasoning algorithms for understanding and predicting failure, recoverying from failure, and learning successful task strategies through adaptive intelligence that can enable generalizability to new scenarios.
- Develop and implement simulation environments for data collection.
- Validate dexterous robot behavior algorithms through comprehensive data analysis and successful demonstrations on hardware.
- Deliver results in accordance with the project timelines.
- Assist with compiling written and oral reports for executives within the company and performing demos.
- Contribute to the portfolio of patents, academic publications, and prototypes to demonstrate research value.
- Collaborate with our teams of scientists and engineers in Honda's regional and global R&D offices, as well as with our partners in academia and industry.
- Assist with supervising interns.
Minimum Qualifications
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- Ph.D. in mechanical engineering, robotics, electrical engineering, computer science, or a related field.
- Expert knowledge on fundamentals of robotics. e.g. robot kinematics and dynamics, control systems, force control, haptics.
- Expert Knowledge on learning-based methods such as reinforcement learning, generative models and inference models.
- Extensive hands-on experience on development and successful implementation of planning, control and recovery algorithms for contact-rich manipulation in real-world.
- Proven experience with one or more physics simulators such as MuJoCo, Isaac Sim, PyBullet, Drake, or Gazebo.
- Familiarity with large models including VLMs, LLMs, and multi-modal foundation models.
- Strong track record of publishing in top robotics venues (e.g T-RO, RA-L, T-M, ICRA, IROS, RSS, CoRL, …)
- Solid programming skills in C++ or Python and strong familiarity with ROS.
- Self-motivated to advance the project and deliver on-time.
- Strong written and oral communication skills including development and delivery of presentations, papers, and technical documents.
Bonus Qualifications
- Strong familiarity with both model and learning-based approaches for manipulation.
- Extensive experience with algorithm and software development (and debugging) for complex robotic systems involving design, implementation and integration of multiple planners, controllers, and perception systems.
- Research experience and expertise in planning and control for dexterous manipulation with tactile robotic multi-fingered hands.
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| Desired Start Date |
3/2/2026 |
| Contract Duration |
3 years |
| Position Keywords |
Embodied AI, Robotics, Scene Understanding, Teleoperation, Dexterous Manipulation, Computer Vision |
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