Home Robot
Home robots are increasingly researched to assist with household tasks and caregiving, aiming to create affordable, versatile systems capable of adapting to diverse environments and user preferences. Current research focuses on improving robot autonomy through lightweight large language models (LLMs) and vision-language models (VLMs) for task planning and execution, often incorporating interactive learning and human-in-the-loop mechanisms to enhance robustness and personalization. These advancements are significant for improving the efficiency and reliability of home robots, potentially impacting the lives of elderly or disabled individuals and relieving the burden on caregivers.
Papers
Dual-arm Motion Generation for Repositioning Care based on Deep Predictive Learning with Somatosensory Attention Mechanism
Tamon Miyake, Namiko Saito, Tetsuya Ogata, Yushi Wang, Shigeki Sugano
Nearest Neighbor Future Captioning: Generating Descriptions for Possible Collisions in Object Placement Tasks
Takumi Komatsu, Motonari Kambara, Shumpei Hatanaka, Haruka Matsuo, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi, Komei Sugiura