Papers
1
Total Citations
33
H-Index
1
About
Evan Prianto is a leading researcher in intelligent robotic systems, with a primary focus on motion planning and control for complex manipulators. His most influential work tackles the challenging problem of path planning for multi-arm manipulators operating in dynamic environments, particularly those with periodically moving obstacles—a scenario common in industrial automation but often overlooked in prior research. In his highly cited 2021 paper, Prianto introduced a novel deep reinforcement learning-based framework that enables robotic arms to adaptively navigate around both static and periodic obstacles in real time. This contribution has garnered 33 citations, reflecting its significance in advancing practical robotics. By bridging reinforcement learning with multi-arm coordination, Prianto’s work not only enhances safety and efficiency in manufacturing but also opens new avenues for human-robot collaboration in cluttered workspaces. His research is widely recognized for its direct applicability to real-world automation challenges, making him a key figure in the field of intelligent robotics and autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1