Robot Learning Intern — Manipulation Policy Learning (2026 Fall)

Anyware Robotics•Fremont, CA

About The Position

Anyware Robotics builds general-purpose mobile manipulator robots for industrial applications. Our robots are deployed in real warehouse and logistics environments, supporting applications such as truck unloading, mobile palletizing, and machine tending. The robots run on AnywareOS, our industrial physical intelligence system. We are looking for a Robot Learning Intern to work on applied manipulation learning using real robot data collected from production and in-house operations. This role is designed for a strong, independent intern who can take a scoped robot learning problem from data understanding to model training, evaluation, and technical recommendation. Example directions include vision-language-action models, imitation learning, diffusion policies, action prediction, failure-mode analysis, or policy evaluation using multimodal robot data.

Requirements

Current MS or PhD student in robotics or machine learning.
Experience with robot learning, imitation learning, reinforcement learning, diffusion policies, VLA models, or visuomotor policy learning.
Strong Python programming skills and hands-on experience with PyTorch, JAX, or similar ML frameworks.
Strong data debugging skills, including dataset inspection, failure analysis, and metric design.
Familiarity with ROS, ROS2, robot logs, RGB-D data, point clouds, or robot kinematics.
Comfortable reading papers, implementing baselines, running experiments, and communicating technical tradeoffs.

Nice To Haves

Experience working with real robot data, not only simulation.
Prior work on manipulation, grasping, or mobile manipulation robotics.
Experience with large pretrained models, vision-language models, or multimodal representation learning.
Publications, open-source projects, or strong course/research projects in robot learning.

Responsibilities

Work with real robot datasets, including various sensory inputs and system information related to robot behavior and outcomes.
Develop and evaluate robot learning models for a range of manipulation-related tasks.
Design, implement, and deliver approaches that bridge exploration and production readiness with imitation learning, diffusion policy, VLA models, or representation learning.
Build data pipelines, training scripts, evaluation metrics, and experiment reports.
Analyze successful and failed robot trials to identify learnable patterns and production-relevant failure modes.
Collaborate with planning, perception, and controls engineers to understand how learned models could augment the production system.
Present clear technical findings, including what worked, what failed, and what should be tested next.