Kyle Lockwood
Papers
3
Total Citations
9
H-Index
2
About
Kyle Lockwood is a rising researcher in human-robot interaction, specializing in the development of fluid, intuitive physical collaboration between humans and machines. His work focuses on the critical challenge of **human-robot handovers**, aiming to replace rigid, robotic exchanges with the seamless, anticipatory movements characteristic of human-to-human interaction. Lockwood’s major contributions lie in modeling human motion intent. He has pioneered the use of **Gaussian Process-based models** to predict human trajectories and **submovement decomposition** for trajectory planning, enabling robots to anticipate a partner’s actions and initiate movement early, mirroring the smooth velocity profiles seen in natural handovers. His research also addresses the practical need for **real-time object localization** using low-cost RGB cameras, making these systems more accessible. While his most-cited works (2022-2023) are early in their impact, with papers accumulating 2-4 citations, they represent foundational steps toward a future where robots can work alongside humans with the grace and predictability of a trusted partner.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Real-Time Object Localization for Human-Robot Handover2 citations · 2023