Sayan Paul
Papers
5
Total Citations
61
H-Index
3
About
Sayan Paul is a robotics researcher whose work bridges the critical gap between physical manipulation and intelligent perception. His primary research areas include robotic bin packing, embodied AI, and human-robot interaction, with a strong focus on developing practical systems for real-world deployment. Paul’s most impactful contribution is the Jampacker system, which introduced an efficient and reliable solution for packing cuboid objects using a robotic arm—a key challenge in Industry 4.0. This work, which has garnered 31 citations, presents the novel Jampack offline 3D bin packing algorithm that significantly improves packing efficiency. He also tackles the fundamental problem of language grounding in robotics with DoRO (Disambiguation of Referred Object for Embodied Agents), earning 19 citations for its approach to resolving ambiguity in task instructions. Beyond these core contributions, Paul has explored learning collaborative actions from YouTube videos and developed Teledrive, an embodied AI telepresence system designed to democratize remote caregiving for the elderly. His work on edge-centric telepresence avatars further demonstrates his commitment to practical, distributed robotic systems. Through these diverse projects, Paul is advancing the frontier of robots that can both understand complex instructions and physically interact with their environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2DoRO: Disambiguation of Referred Object for Embodied Agents19 citations · 2022
- 3Learning Collaborative Action Plans from YouTube Videos5 citations · 2022
- 4Teledrive: An Embodied AI Based Telepresence System3 citations · 2024
- 5