Kanata Suzuki - LLM for Robot Learning

The starred authors(*) are co-first authors and contributed equally.

Integration of Robot Motion and LLM based on Predictive Coding Approach

In progress

Kanata Suzuki, Tetsuya Ogata: Sensorimotor Attention and Language-based Regressions in Shared Latent Variables for Integrating Robot Motion Learning and LLM, Proceedings of 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'24), pp.11872-11878, acceptance rate 47.5%, Abu Dhabi, UAE, October 14-18, 2024.
- DOI: https://doi.org/10.1109/IROS58592.2024.10802349

Interactively Task Planning with Uncertainty Analysis and Questioning

In progress

Kazuki Hori, Kanata Suzuki, Tetsuya Ogata: Interactively Robot Action Planning with Uncertainty Analysis and Active Questioning by Large Language Model, Proceedings of 2024 IEEE/SICE International Symposium on System Integrations (SII'24), pp.85-91, Ha Long, Viet Nam, January 8-11, 2024.
- DOI: https://doi.org/10.1109/SII58957.2024.10417267

Bidirectional Translation between Language and Motion

Interaction using natural language is important in collaborative tasks between humans and robots. With the development of LLM, research on robot motion planning and environment recognition is being actively conducted, and the realization of human-collaborative robots is expected. While LLM is an important tool for connecting traditional robotics technology with the real world, in many previous studies, LLM and robot motion controllers have been separated, and the prediction results of the model's predicted results may contradict reality. This research aims to link a language model and a robot motion generation model, and establish a robot control model that can interactively respond to unknown situations by transforming the pre-trained word embeddings to embodied ones [1] with a small amount of motion-language paired data [2].

Minori Toyoda, Kanata Suzuki, Hiroki Mori, Yoshihiko Hayashi, Tetsuya Ogata: Embodying pre-trained word embeddings through robot actions, IEEE Robotics and Automation Letters, vol.6, no.2, pp.4225-4232, 2021 (presented at ICRA'21).
- DOI: https://doi.org/10.1109/LRA.2021.3067862
Minori Toyoda*, Kanata Suzuki*, Yoshihiko Hayashi, Tetsuya Ogata: Learning Bidirectional Translation between Descriptions and Actions with Small Paired Data, IEEE Robotics and Automation Letters, vol.7, no.4, pp.10930-10937, 2022 (presented at IROS'22).
- DOI: https://doi.org/10.1109/LRA.2022.3196159