Deep predictive learning: Motion learning concept inspired by cognitive robotics

K Suzuki, H Ito, T Yamada, K Kase, T Ogata - arxiv preprint arxiv …, 2023 - arxiv.org
Bridging the gap between motion models and reality is crucial by using limited data to
deploy robots in the real world. Deep learning is expected to be generalized to diverse …

Interactively robot action planning with uncertainty analysis and active questioning by large language model

K Hori, K Suzuki, T Ogata - 2024 IEEE/SICE International …, 2024 - ieeexplore.ieee.org
The application of the Large Language Model (LLM) to robot action planning has been
actively studied. The instructions given to the LLM by natural language may include …

Sensorimotor Attention and Language-based Regressions in Shared Latent Variables for Integrating Robot Motion Learning and LLM

K Suzuki, T Ogata - … on Intelligent Robots and Systems (IROS), 2024 - ieeexplore.ieee.org
In recent years, studies have been actively conducted on combining large language models
(LLM) and robotics; however, most have not considered end-to-end feed-back in the robot …

Multi-Timestep-Ahead Prediction with Mixture of Experts for Embodied Question Answering

K Suzuki, Y Kamiwano, N Chiba, H Mori… - … Conference on Artificial …, 2023 - Springer
In this study, we propose a method that integrates visual field predictions with different time
scales and investigates its effectiveness for embodied question answering (EQA). In EQA, it …

Enhancement of Long-Horizon Task Planning via Active and Passive Modification in Large Language Model

K Hori, K Suzuki, T Ogata - 2024 - researchsquare.com
This study proposes a method for generating complex, long-horizon off-line task plans using
large language models (LLMs). Although several studies have been conducted in recent …

能動的推論を参考とした実ロボットの動作生成

尾形哲也 - 人工知能, 2023 - jstage.jst.go.jp
例としてルービックキューブを Shadow Hand を用いて学習させる試みを行っていた. しかしながら,
実環境での類似の困難に直面し, 2021 年に一時的な撤退を経験している. しかし今年 2023 …