5 papers
Bi-VLA: Bilateral Control-Based Imitation Learning via Vision-Language Fusion for Action Generation
Masato Kobayashi, Thanpimon Buamanee
We propose Bilateral Control-Based Imitation Learning via Vision-Language Fusion for Action Generation (Bi-VLA), a novel framework that extends bilateral control-based imitation le…
Bi-LAT: Bilateral Control-Based Imitation Learning via Natural Language and Action Chunking with Transformers
Takumi Kobayashi, Masato Kobayashi, Thanpimon Buamanee +1
We present Bi-LAT, a novel imitation learning framework that unifies bilateral control with natural language processing to achieve precise force modulation in robotic manipulation.…
MRHaD: Mixed Reality-based Hand-Drawn Map Editing Interface for Mobile Robot Navigation
Takumi Taki, Masato Kobayashi, Eduardo Iglesius +3
Mobile robot navigation systems are increasingly relied upon in dynamic and complex environments, yet they often struggle with map inaccuracies and the resulting inefficient path p…
ALPHA- and Bi-ACT Are All You Need: Importance of Position and Force Information/Control for Imitation Learning of Unimanual and Bimanual Robotic Manipulation with Low-Cost System
Masato Kobayashi, Thanpimon Buamanee, Takumi Kobayashi
Autonomous manipulation in everyday tasks requires flexible action generation to handle complex, diverse real-world environments, such as objects with varying hardness and softness…
DABI: Evaluation of Data Augmentation Methods Using Downsampling in Bilateral Control-Based Imitation Learning with Images
Masato Kobayashi, Thanpimon Buamanee, Yuki Uranishi
Autonomous robot manipulation is a complex and continuously evolving robotics field. This paper focuses on data augmentation methods in imitation learning. Imitation learning consi…