Call for Interns

OMRON SINIC X (OSX) is looking for research interns throughout the year to work with our members in challenging research projects on a variety of topics related to robotics, machine learning, computer vision, and HCI. Many students have participated in our internship program, and their achievements have been published as academic papers at international conferences such as CVPR, ICML, IJCAI, ICRA, CoRL, or as OSS libraries. For more information about our activities at OSX, please visit Medium and GitHub

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Research on integration of physical or algorithmic principles into machine learning

In this research project, we aim to develop novel models and methods that seamlessly integrate machine learning with classical problems窶敗uch as physics simulations and mathematical optimization窶杯hat are grounded in physical and algorithmic principles. Based on the research outcomes, we will prepare and submit a paper targeting top-tier international conferences in the field of machine learning, such as NeurIPS, ICML, and ICLR.

  • Required skills and experience
    • Experience with deep learning, either through research or reproducing existing methods
    • Either 1) good understanding about geometric deep learning or 2) a Bachelor's degree or higher in physics or mathematics
    • Python development skills
  • Preferred skills and experience
    • Knowledge of geometric mathematics, such as spherical harmonics and rotational equivariance
    • Background in physics or mathematics (e.g., Bachelor's degree in physics or math)
    • Research and/or developement experience in geometric deep learning
    • Skills in implementing custom forward/backward functions and custom GPU kernels (e.g., with PyTorch)
    • Proficiency in C++
    • Experience using GitHub/GitLab and Docker
  • Machine learning
  • Algorithm
  • Geometric deep learning
  • Physics

Soft robotic foundation model

"We are developing methods that enable robots to quickly adapt to various tasks by leveraging emerging robot foundation models and soft robotics technologies. Accepted interns will be involved in activities such as surveying robot foundation models, integrating pretrained models, exploring adaptation methods for novel environments, conducting experiments, and writing research papers.
The project also includes robust robot manipulation strategies using language, such as language-based error recovery. Our goal is to submit papers to top-tier international conferences and journals in robotics and machine learning (e.g., CoRL, RSS, RA-L, T-RO, IJRR, ICRA, IROS).
Interns will work closely with their mentor, meeting at least once per week to track progress, plan for paper submissions, and coordinate writing tasks to maximize the chances of successful publication.
While the following topics are particularly relevant, the scope is not limited to them; project themes will be flexibly tailored to the intern窶冱 expertise. We especially welcome interns with experience using or developing large language models (LLMs) and a strong interest in their application to robotics. This project is expected to be conducted on-site in Tokyo.
- Soft robotic manipulation.
- Robot foundation models.
- Language-guided robot manipulation

  • Required skills and experience
    • Experience in Python or C++
      At least one of the following:
    • Research and development experiences in robot learning, sensing, control theory, or motion planning
    • Research and development experiences using machine learning or large language models
  • Preferred skills and experience
    • Research and development experience in robot foundation models
    • Research and development experience in ROS
    • Experience in submitting papers of the field of robotics and artificial intelligence
    • Experience in participation in robot competitions
  • Machine learning
  • Robotics
  • Computer vision
  • Signal processing
  • Natural language processing
  • Algorithm
  • Development
  • Soft robotics
  • Robot foundation model

Open World Manipulation Benchmark

In this project, we are developing a benchmark to evaluate a robot窶冱 ability to perform fully autonomous object manipulation based on natural language instructions, even in previously unseen environments, situations, objects, and tasks.

  • Required skills and experience
    • Experience in research on robot manipulation or benchmark development
    • Basic knowledge of Vision-Language Models and VideoLM
  • Preferred skills and experience
    • Experience with having papers accepted at international conferences
    • Research on benchmark development
  • Machine learning
  • Robotics
  • Computer vision
  • Natural language processing
  • Development

Knowledge Distillation of Large Language Models

While large-scale language models have garnered significant attention, yet their increasing model sizes pose challenges for individual use. This project aims to research new methods of knowledge distillation that can achieve comparable performance to general-purpose models within specific domains. The project results are expected to contribute to paper submissions at international conferences and to be released as a widely impactful library.

  • Required skills and experience
    • experience in deep learning using PyTorch, etc
    • knowledge and experience in Transformers, RAG and LLMs
  • Preferred skills and experience
    • Publication recors in the field of machine learning (ICML,NeurIPS,ICLR) , natural language processing (ACL, EMNLP, NAACL) and relevant fields (SGIR,KDD,ICDE,VLDB,SIGMOD)
  • Machine learning
  • Natural language processing
  • Data Mining

Body Integration Learning Framework for Advanced Skilled Robots

Through human-robot diverse interactions, named Body Integration Learning, we aim to equip robots with advanced skills to carry out challenging tasks. Selected interns will work on implementing robot learning algorithms based on imitation learning and reinforcement learning, running experiments, and contributing to paper writing.
Our goal is to submit the outcomes of this robotics ﾃ HCI fusion research to top-tier international venues (e.g., CoRL, ICRA, IROS, HRI, CHI). Mentors will meet with interns at least once per week to review research progress, plan submissions, coordinate writing tasks, and increase the likelihood of successful publication.
The following topics are relevant (non-exhaustive). We will tailor the specific theme to each intern窶冱 background. We actively welcome interns with R&D experience in robot motion learning and a strong interest in cross-disciplinary HCI research窶蚤nd vice versa.
- Imitation learning
- Reinforcement learning
- Learning from human feedback and interaction (human-in-the-loop)
This project assumes on-site work in Tokyo.

  • Required skills and experience
    • Experience in Python or C++
      At least one of the following:
    • Research and development experiences in robot learning, sensing, control theory, or motion planning
    • Research and development experiences in human-computer interaction or virtual reality
  • Preferred skills and experience
    • Research and development experience in ROS
    • Experience in submitting papers in the field of robotics and HCI
    • Experience in participation in robot competitions or exhibitions
  • Machine learning
  • Robotics
  • Interaction
  • Signal processing
  • Algorithm
  • Development

Embedding Model for Foundation Models

We are seeking research interns to develop novel representation learning methods for efficiently compressing large-scale text corpora and 3D spatial data. This project focuses on building new embedding databases that can be utilized by applications such as LLMs and robotic foundation models. Research outcomes will target publication at top-tier international conferences and aim for release as widely-used open-source libraries.

  • Required skills and experience
    • Implementation experience with deep learning using PyTorch or JAX
    • Knowledge of Transformer models
    • Fundamental knowledge of information retrieval and embedding representations
  • Preferred skills and experience
    • Experience fine-tuning LLMs (T5, BERT, etc.)
    • Knowledge of 3D representation methods such as NeRF and Gaussian Splatting
    • Experience with multimodal models such as CLIP
    • Knowledge of Normalizing Flows and invertible neural networks
    • Experience with low-level optimization using CUDA or Triton
  • Machine learning
  • Robotics
  • Computer vision
  • Natural language processing
  • Algorithm

Robot Manipulation Alignment

In this project, we will explore robot manipulation aligment.

  • Required skills and experience
    • Experience in research on robot manipulation or benchmark development
    • Basic knowledge of Vision-Language Models and VideoLM
  • Preferred skills and experience
    • Experience with having papers accepted at international conferences
    • Research on benchmark development
  • Machine learning
  • Robotics
  • Computer vision
  • Natural language processing
  • Development

Lightweight and compact manipulator mechanism design

We will develop a design method for a lightweight and compact manipulators, such as the manipulator we developed in the past (https://omron-sinicx.github.io/twistsnake/).

  • Required skills and experience
    • Experience in robotic mechanism design and 3D printers
  • Preferred skills and experience
    • Robot competition
    • Experience in team-based development
    • Experience of receiving an award from an academic society or a scholarship
    • Experience of coding in ROS, Python, or C++ in the development of a robot
  • Robotics
  • Mechanics design

Learning of object manipulation skills generalized to objects and tasks

We will develop a method for learning of object manipulation skills generalized to objects and tasks.

  • Required skills and experience
    • Publication record in robot learning (ICRA, IROS, CoRL, ICML, NeurIPS, ICLR, etc.)
    • Experience in manipulation learning
  • Preferred skills and experience
    • Management of projects and code using git/GitLab/GitHub
    • Experience in team-based development
  • Machine learning
  • Robotics
  • Manipulation
  • Imitation learning

Learning Manipulation with Segmentation Using Large-Scale Language Models

We will develop a method for learning manipulation with segmentation using Large-Scale Language Models

  • Required skills and experience
    • Publication record in robot learning (ICRA, IROS, CoRL, ICML, NeurIPS, ICLR, etc.)
    • Experience in learning
  • Preferred skills and experience
    • Management of projects and code using git/GitLab/GitHub
    • Experience in team-based development
  • Machine learning
  • Robotics
  • Manipulation
  • Large language model

Research on robust image recognition model and multi-modal language model

Image recognition models are noted to be vulnerable to domain shifts caused by environmental changes. This project aims to construct robust image recognition models resilient to environmental changes. We plan to select research topics on robust image recognition models from various fields, including but not limited to generalization of image classification models and applications such as Vision-Language models, and aim to submit them to international conferences.

  • Required skills and experience
    • Experience in Python development
    • Experience in developing machine learning model for image recognition
  • Preferred skills and experience
    • Knowledge of image recognition model
    • Knowledge of transfer learning and multi-modal model
    • Experience writing papers in related fields
  • Machine learning
  • Computer vision
  • Natural language processing
  • Domain Generalization
  • Image recognition
  • Multimodal model

AI for Science

You will work on AI research that accelerates and automates research and development itself. You will participate in partial projects in the realization of AI scientists who can formulate research claims, run experiments, analyze the results, and write papers in an interactive co-evolution with human researchers.

  • Required skills and experience
    • Python, Github, Docker
    • Knowledge and experience in deep learning
  • Preferred skills and experience
    • Knowledge and experience in natural language processing, computer vision, and data science
    • Mathematical knowledge and formulation ability in machine learning and deep learning
  • Interaction
  • Algorithm
  • Development

Research on 3D vision including visual SLAM and NeRF

In this research project, we aim to develop novel models and methods for image-based 3D sensing technologies, such as Visual SLAM and NeRF. Based on the research outcomes, we will prepare and submit a paper targeting top-tier international conferences in the field of computer vision, such as CVPR, ICCV, and ECCV.

  • Required skills and experience
    • Experience with deep learning, either through research or reproducing existing methods
    • Good mathematical understanding about 3D geometries (e.g., perspective projection, rotation, translation, etc.)
    • Python development skills
  • Preferred skills and experience
    • Knowledge and experience in 3D deep learning or classical VSLAM
    • Knowledge and experience in munerical optimization
    • Skills in implementing custom forward/backward functions and custom GPU kernels (e.g., with PyTorch)
    • Proficiency in C++
    • Experience using GitHub/GitLab and Docker
  • Machine learning
  • Computer vision
  • 3D vision
  • Optimization

Study on multi-agent path planning algorithms from the topological viewpoint

We will study approaches to multi-agent path planning from the topological viewpoint. Accepted interns are expected to work in collaboration with the mentors to submit research results to top international conferences in the field of artificial intelligence and machine learning.

  • Required skills and experience
    • Basic knowledge in topological geometry and computational geometry
  • Preferred skills and experience
    • Research and development experiences on path planning, especially multi-agent path planning
    • Publication record in the field of artificial intelligence (e.g., AAAI, IJCAI, AAMAS, ICML, NeurIPS, ICLR)
    • Expert knowledge in topological geometry and computational geometry
  • Algorithm
  • Path planning
  • Multi-agent system

Research on application of machine learning to physics simulation method and results

We will conduct research and development on the application of machine learning to the algorithms or the data analysis of physics simulations, e.g. DFT/MD/Tensor Network, and write papers to journals e.g. Nature/Science, Physical Review, or conferences e.g. SC/ICML.

  • Required skills and experience
    • Knowledge and experience in physics simulation e.g. DFT/MD/Tensor Network
  • Preferred skills and experience
    • Knowledge and experience in machine learning
    • Pytorch, Python
    • Github, Docker
  • Interaction
  • Algorithm
  • Development
  • Physics simulation

Research on application of machine learning methods to physics simulation

We will conduct research and development on the application of your machine learning methods to physics simulations and write papers in related fields (Nature/Science, Physical Review, SC/HPCG, etc.).

  • Required skills and experience
    • Knowledge and experience in machine learning
  • Preferred skills and experience
    • Knowledge and experience in physics simulation e.g. DFT/MD/Tensor Network
    • Pytorch, Python
    • Github, Docker
  • Interaction
  • Algorithm
  • Development
  • Physics simulation

Robot software or hardware engineering

We invite accepted interns to contribute to ongoing research projects in robot software or hardware engineering. This may include programming to launch robotic systems, designing tools and environments for robots, and performing hardware maintenance tasks.

We also encourage interns to publish the outcomes of their engineering work as original or systems research papers, or to release open-source software libraries as part of their achievements during the internship.

  • Required skills and experience

    At least one of the following:

    • Research or development experience in ROS
    • Research or development experience in robot hardware design
    • Research or development experience in sesing design
  • Preferred skills and experience
    • Experience in participation in robot competitions
  • Robotics
  • Development
  • Manipulation
  • Mechanics design
  • OSS development

Robotic Manipulation Using Language Models

We will develop a method to generate robot manipulation using language models.

  • Required skills and experience
    • Knowledge and experience in language-model research
  • Preferred skills and experience
    • Knowledge and experience in robot motor-skill learning with physics simulation
    • Ability to program in Python using PyTorch
    • Proficiency with tools such as GitHub and Docker
  • Machine learning
  • Robotics
  • Computer vision
  • Natural language processing
  • Physical simulation

Learning soft robotic tool manipulation with tactile sensors

The goal of this project is to enable robots equipped with physically soft bodies and tactile sensing to learn complex tool manipulation tasks, thereby elucidating the synergies among soft robots, tactile sensing, and motor learning.
To this end, developing a tactile-based learning framework, employing data-efficient learning approaches, and exploring strategies for learning from sub-optimal datasets will be essential.

We would like the accepted interns to develop the robot learning algorithm with tactile sensor fusion, implement robot software, perform experiments, and write papers.
We aim to submit top-tier robotics or AI conference or journal papers such as (ICRA, IROS, CoRL, RA-L, T-RO, and NeurIPS). The mentors will meet with the interns at least once a week to discuss the research progress, plans for paper submissions, and distribution of writing tasks to ensure a more reliable submission.

The following themes are relevant but not limited. Themes will be flexibly determined based on the intern's expertise. The project actively seeks interns with experience in machine learning, reinforcement learning development, and a strong interest in robotics applications. The project assumes on-site work in Tokyo.
- Soft robotic manipulation learning
- Tactile-based manipulation using vision-based or distributed tactile sensors
- Offline and online reinforcement learning, and Imitation learning
- Language-guided manipulation

Accepted papers that interns contributed as first author advised by this mentor: IROS2024, ICRA2024 (2 papers), IROS 2023 (2 papers), NeurIPS 2023, IEEE ACCESS, IEEE CASE 2021, and CoRL 2020.
Related projects: learning robotics assembly using soft wrist and tactile sensors. https://omron-sinicx.github.io/saguri-bot-page/, https://omron-sinicx.github.io/symmetry-aware-pomdp/, https://omron-sinicx.github.io/soft-robot-sim-to-real/

  • Required skills and experience
    • Experience in Python or C++
      At least one of the following:
    • Research and development experiences in robot learning, sensing, control theory, or motion planning
    • Research and development experiences using machine learning or reinforcement learning
  • Preferred skills and experience
    • Research and development experience in ROS
    • Experience in submitting papers of the field of robotics and artificial intelligence
    • Experience in participation in robot competitions
  • Machine learning
  • Robotics
  • Computer vision
  • Signal processing
  • Algorithm
  • Development
  • Soft robotics
  • Tactile sensing

Investigating and Applying the Cognitive Mechanisms of Robot Embodiment

This project explores the cognitive mechanisms by which individuals perceive robotic systems as extensions of their own bodies (embodiment). By combining psychophysical experiments and machine learning, we aim to identify the key factors that contribute to embodiment and examine their potential applications. The project also includes submitting research findings to top-tier HCI conferences (e.g., CHI, UIST) and high-impact journals (e.g., Science, Nature). This project requires on-site work in Tokyo.
Related project: Swarm Body (CHI'24) https://medium.com/sinicx/-69bc10abfd64

  • Required skills and experience
    • Knowledge and experience in deep learning
    • Understanding of embodiment in psychology (e.g., sense of agency, sense of body ownership)
    • Skills and experience in developing VR systems using Unity
  • Preferred skills and experience
    • Research paper presentations in HCI, CV, ML, or robotics
    • Designing and conducting user studies
    • Basic knowledge of statistics
    • Experience developing robots or hardware
  • Machine learning
  • Robotics
  • Computer vision
  • Interaction
  • Embodied cognition
  • Sense of agency
  • World model

Robot Learning using large-scale skill database

We are seeking interns to work on novel methods of imitation learning and offline reinforcement learning that leverage past unlabeled demonstrations, enabling even small models to achieve high performance. Our focus includes developing databases that efficiently accumulate and retrieve skills from vast experience datasets, and methods for combining past skills to adapt to new tasks. The research outcomes aim to be published in international conferences and released as a widely impactful library.

  • Required skills and experience
    • Research Experiences using PyTorch
    • Research and development experiences in machine learning
  • Preferred skills and experience
    • Publication recors in the field of robotics(CoRL,ICRA,IROS),machine learning (ICML,NeurIPS,ICLR) and relevant fields (SGIR)
  • Machine learning
  • Robotics
  • Computer vision

Conditions

Term: From 3-month duration (assuming 5 working days a week). Start and end dates can be adjusted. Some projects accept short-term interns from 1-month duration.
Hours: Full-time or part-time (e.g. 3 days a week, etc. negotiable). 45-minute breaks. Holidays and weekends off.
Location: On-site, hybrid, or remote options are available. Hybrid and remote options are only available if you live in Japan; due to legal issues, we cannot pay salaries to remote interns who live outside of Japan. If you join our internship program, you must come to Japan. In such cases, we offer support for travel expenses. Some internship projects may require on-site work. In this case, you will be assigned to one of our offices in Hongo or Shinagawa.
Salary: Full-time monthly salary ranges from 240,000 JPY to 480,000 JPY. Hourly rate is applied for part-time work. Social security and other benefits are provided according to the working conditions. Transportation and housing expenses are fully covered. In addition, other expenses necessary for research activities (PC, laptop, etc.) are fully supported.
Language: Japanese or English (English-only communication is also fine.)
Others: Two or more mentors with extensive research experience will provide in-depth support for each project. Computational resources (workstations, and server clouds with GPUs) and robotic facilities (robotic arms, various sensors, 3D printers, motion capture systems, and other prototyping and experimental equipment) are available.

How to apply

Please fill the application form. We will first screen each application based on those information.
For other inquires, please contact internships@sinicx.com. We will first screen each application based on those information.

Those who pass the above screening will be interviewed remotely. Please prepare slides or other materials to introduce past research and development activities and achievements.

Please contact us at least three months in advance if you need a visa to enter Japan.

Explorer Activities