Ashesh Jain
Papers
7
Total Citations
382
H-Index
6
About
Ashesh Jain is a robotics and machine learning researcher whose work sits at the intersection of robot learning, human-robot interaction, and intelligent planning systems. He is perhaps best known for his contributions to the RoboBrain project (2014), a large-scale knowledge engine designed to enable robots to learn and share representations across diverse tasks and data modalities — a landmark contribution that has garnered over 110 citations. Much of Jain's research addresses a deceptively difficult challenge: teaching robots to understand human preferences. Through his development of co-active learning frameworks, he demonstrated how robots can iteratively refine trajectory planning based on user feedback, with foundational papers on this topic accumulating nearly 160 citations combined. His PlanIt system extended this vision by leveraging crowdsourcing to learn path preferences at scale, while his work on anticipatory planning for human-robot teams tackled the critical problem of proactive collaboration. Later contributions applied recurrent neural networks to sensory-fusion architectures for driver activity anticipation, showcasing his range beyond manipulation robotics. Across his portfolio, Jain has consistently advanced the goal of robots that are not merely functional, but genuinely responsive to human context and intent.
Research Focus
Key Achievements
Top Papers
- 1RoboBrain: Large-Scale Knowledge Engine for Robots110 citations · 2014
- 2Learning preferences for manipulation tasks from online coactive feedback97 citations · 2015
- 3Anticipatory Planning for Human-Robot Teams69 citations · 2015
- 4Learning Trajectory Preferences for Manipulators via Iterative Improvement59 citations · 2013
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