David Rovick Arrojo
Papers
2
Total Citations
360
H-Index
2
About
David Rovick Arrojo is a leading researcher in robot learning, with a primary focus on developing scalable benchmarks and simulation environments that accelerate progress in embodied AI. His most significant contribution is the creation of **RLBench**, a comprehensive benchmark and learning environment that has become a cornerstone of the field. The 2020 paper introducing RLBench has garnered **over 315 citations**, reflecting its widespread adoption. RLBench features **100 unique, hand-designed tasks**—from simple reaching to complex multi-stage manipulations like opening an oven and placing a tray—providing a rigorous and standardized testbed for evaluating robot learning algorithms. This work has been instrumental in moving the community beyond simple toy problems toward more realistic, dexterous manipulation challenges. Arrojo’s contributions have helped define how researchers measure and compare progress in reinforcement learning for robotics, making RLBench a go-to resource for labs worldwide. His work continues to shape the trajectory of robot learning by enabling reproducible, challenging, and meaningful evaluation.
Research Focus
Key Achievements
Top Papers
- 1RLBench: The Robot Learning Benchmark & Learning Environment315 citations · 2020
- 2RLBench: The Robot Learning Benchmark & Learning Environment45 citations · 2019