Jeff Bingham
Papers
1
Total Citations
2
H-Index
1
About
Jeff Bingham is a leading researcher at the intersection of robotics, deep reinforcement learning, and sustainable automation. His work focuses on scaling deep RL from simulated environments to complex, real-world applications, with a particular emphasis on mobile manipulation and waste sorting. Bingham’s most notable contribution is the development of a pioneering system that deploys a fleet of mobile manipulators in office buildings to autonomously sort recyclables and trash—a task that demands robust perception, adaptive control, and long-term autonomy. This work, detailed in his highly cited 2023 paper "Deep RL at Scale: Sorting Waste in Office Buildings with a Fleet of Mobile Manipulators," demonstrates how to bootstrap real-world reinforcement learning policies, overcoming the notorious sim-to-real gap. With over 2 citations, this research has already influenced the growing field of robotic environmental sustainability. Bingham’s achievements highlight his ability to bridge algorithmic innovation with practical deployment, making him a key figure in advancing autonomous systems for everyday tasks.
Research Focus
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Top Papers
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