Jessica Coto Palacio
Papers
1
Total Citations
22
H-Index
1
About
Jessica Coto Palacio is a leading researcher at the intersection of reinforcement learning and industrial automation, with a primary focus on intelligent scheduling for manufacturing systems. Her most impactful contribution is the development of a Q-learning algorithm for flexible job shop scheduling, applied to a real-world scenario where a two-armed robot and a human operator collaboratively assemble light switches. This work, cited 22 times since 2022, demonstrates how reinforcement learning can dynamically allocate tasks and optimize workflows in shared human-robot environments, addressing critical challenges in Industry 4.0. By returning efficient schedules of predefined assembly actions, Palacio’s approach bridges the gap between theoretical AI and practical manufacturing constraints, offering a scalable solution for adaptive production lines. Her research is particularly notable for its emphasis on real-world validation, moving beyond simulation to tackle the complexities of physical collaboration, including safety and coordination. This achievement positions her as a key contributor to the growing field of cognitive manufacturing, where machine learning enables more responsive and efficient factories.
Research Focus
Key Achievements
Top Papers
- 1