Research Intern: Closed-loop E2E Driving

Job Number: P25INT-57 Honda Research Institute USA (HRI-US) is seeking a talented and motivated PhD-level research intern to explore methods to improve closed-loop planning performance — spanning policy learning, trajectory generation, and closed-loop training/evaluation — and validate them in state-of-the-art simulation benchmark. This internship is ideal for PhD students with a strong publication record in deep learning and planning who are looking to apply their research to practical robotics and autonomous systems. Mountain View, CA **Key Responsibilities** * Research and develop planning modules for E2E driving models, including trajectory scoring/selection, diffusion- or anchor-based trajectory generation, and multi-modal planning heads * Design closed-loop training strategies (e.g., closed-loop fine-tuning, RL/IL hybrid post-training, world-model-based rollout training) to mitigate distribution shift and compounding errors * Build and run closed-loop evaluation pipelines on benchmarks such as NAVSIM, Bench2Drive, CARLA Leaderboard, or nuPlan; analyze failure modes and drive iterative improvement * Conduct ablations and large-scale experiments; profile and optimize training/inference throughput on multi-GPU clusters * Collaborate with full-time researchers and engineers; present findings internally and contribute to publications and/or patents **Minimum Qualifications** * Currently enrolled in a PhD program in Robotics, Computer Science, Electrical Engineering, or a related field. * Solid foundation in deep learning and strong proficiency in Python and PyTorch. * Understanding of motion planning and/or policy learning fundamentals (imitation learning, reinforcement learning, or trajectory optimization). * Experience training and debugging neural networks at scale. * Ability to read, implement, and extend recent research papers independently. **Bonus Qualifications** * Publications at top-tier venues (CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, CoRL, ICRA, RSS) * Hands-on experience with E2E driving frameworks (e.g., UniAD, VAD, PARA-Drive, DiffusionDrive, Hydra-MDP) or closed-loop benchmarks (NAVSIM, Bench2Drive, CARLA, nuPlan) * Experience with generative planners (diffusion policies, flow matching), world models, or RL post-training of driving policies * Familiarity with simulation infrastructure, sensor simulation, or scenario generation * Track record in autonomous driving challenges/leaderboards (e.g., CARLA Leaderboard, Waymo/nuPlan challenges) * Strong software engineering practices (CUDA/TensorRT optimization, distributed training, CI) **Years of Work Experience Required** 0 **Desired Start Date** 1/25/2027 **Internship Duration** 3 Months **Position Keywords** E2E, Closed loop simulation, Autonomous Driving **Warning:**You do not have the permission to access the upload fields on this form. Contact the form owner or portal administrator to request the access.