Joseph Yaconelli
Papers
1
Total Citations
12
H-Index
1
About
Joseph Yaconelli is a robotics researcher whose work sits at the intersection of machine learning and autonomous systems, with a particular focus on model-based reinforcement learning for low-level control. His most cited paper, "Low-Level Control of a Quadrotor With Deep Model-Based Reinforcement Learning" (2019, 12 citations), tackles a persistent challenge in robotics: the time-consuming, platform-specific tuning of low-level controllers. Yaconelli demonstrated that deep model-based reinforcement learning can learn effective control policies directly from data, reducing the need for manual heuristic parameter tuning and extensive system knowledge. This work offers a pathway toward more adaptable and autonomous robotic systems, particularly in aerial vehicles like quadrotors. While his citation count is still growing, Yaconelli's contribution is notable for bridging the gap between high-level planning and low-level actuation—a critical step for deploying robots in unstructured environments. His research speaks to a broader trend in robotics: moving away from handcrafted controllers toward learned, data-driven approaches that promise faster deployment and greater robustness across diverse platforms.
Research Focus
Key Achievements
Top Papers
- 1