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Johns Hopkins professor Anand Bhattad earns ICRA Best Paper honor
Bhattad holds a bachelor's degree in Civil Engineering from the National Institute of Technology Karnataka (NITK) Surathkal, India.
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Staff Reporter - Thu, 11, Jun 2026
- 04:31 EDT
- 33
Anand Bhattad / Johns Hopkins
Indian American Assistant Professor Anand Bhattad has won the Best Paper Award on Robot Learning at the 2026 IEEE International Conference on Robotics and Automation (ICRA) on June 2.
Bhattad, along with Toyota Technological Institute at Chicago (TTIC) Ph.D. students Tianchong Jiang, Jingtian Ji, and Vincent Xiangshan Tan, Ph.D. alumnus Jiading Fang, and Professor Matthew Walter, won the prestigious award for their research paper, “Do You Know Where Your Camera Is? View-Invariant Policy Learning with Camera Conditioning.”
Bhattad is an assistant professor in the Department of Computer Science at Johns Hopkins University, where he is also a member of the Data Science and AI Institute.
Prior to joining Johns Hopkins, Bhattad served as a research assistant professor (RAP) at the Toyota Technological Institute at Chicago (TTIC), where he collaborated with the Ph.D. candidates and his peers on the paper that won him the ICRA recognition. His research focuses on computer vision, robotics, and machine learning, with particular emphasis on areas such as view-invariant policy learning and robot perception.
He earned his Ph.D. from the University of Illinois Urbana-Champaign (UIUC). During his doctoral studies and early career, he was mentored by prominent researchers including Derek Hoiem, Svetlana Lazebnik, Greg Shakhnarovich, and Shenlong Wang.
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Bhattad also holds a dual master's degree in Computer Science and Civil and Environmental Engineering from the University of Illinois Urbana-Champaign and a bachelor's degree in Civil Engineering from the National Institute of Technology Karnataka (NITK) Surathkal, India.
Breaking down the scientific paper for easier understanding, TTIC said, "Robot policies are typically trained from a fixed camera viewpoint, causing them to fail when the camera moves or is repositioned."
TTIC added, "This work improves viewpoint generalization by conditioning policies on camera position and orientation. To evaluate viewpoint robustness, the team also introduced six new robotic manipulation tasks, released alongside code and demonstrations."
Announcing his win on LinkedIn, Bhattad said, "Honored that our paper received the #ICRA2026 Best Paper Award on Robot Learning."
TTIC also congratulated the team and said, "Congratulations to Tianchong Jiang, Jingtian Ji, Vincent Xiangshan Tan, Jiading Fang, Matthew Walter, Anand Bhattad, and Vitor Guizilini, recipients of the Best Paper Award on Robot Learning at ICRA 2026 for 'Do You Know Where Your Camera Is?'"
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