Tomoki Ando
Papers
2
Total Citations
14
H-Index
2
About
Tomoki Ando is a robotics researcher whose work bridges the gap between machine learning and complex physical manipulation. His primary research areas include collision-free motion planning, dual-arm robotic manipulation, and the application of generative models to robotics. Ando’s most notable contribution is pioneering the first-ever robotic buttoning task using a dual-arm robot, a feat previously considered too complex due to the simultaneous handling of flexible fabric and rigid buttons. This exploratory study, published in 2022, has already garnered 7 citations for its novel marker-based and marker-less machine learning methods. In parallel, Ando has advanced collision-free planning by developing a learning-based approach using Conditional Generative Adversarial Networks (cGANs). His 2023 paper, also with 7 citations, introduces a method to transform robot joint space into a latent space that inherently avoids collisions, enabling trajectory generation based on arbitrary optimization criteria. This work is particularly impactful for real-time robotic applications where safety and efficiency are paramount. Ando’s research is characterized by tackling long-standing manipulation challenges with elegant, data-driven solutions.
Research Focus
Key Achievements
Top Papers
- 1
- 2