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Anand Thobbi is a robotics researcher whose work has significantly advanced the field of human-robot interaction, particularly in collaborative manipulation and imitation learning for humanoid robots. His research focuses on enabling robots to work seamlessly alongside humans, addressing fundamental challenges in role-sharing, motion estimation, and gesture learning. Thobbi's most influential contribution, "An Integrated Framework for Human–Robot Collaborative Manipulation" (2014, 71 citations), established a landmark learning architecture that allows humanoid robots to perform physically cooperative tasks with humans, validated through a joint table-lifting experiment. His complementary 2011 work on human motion estimation (67 citations) tackled the sophisticated problem of dynamic leader-follower role switching — allowing robots to fluidly adapt their collaborative role without pre-programmed assignments, much as humans naturally do. Beyond manipulation, Thobbi made notable contributions to gesture imitation learning, including addressing the technically challenging problem of learning arm gestures from incomplete training data. His two-phase framework combining imitation and reinforcement learning further demonstrated his commitment to biologically inspired, adaptable robotic systems. With over 200 cumulative citations, Thobbi's body of work has meaningfully shaped how researchers approach intuitive, flexible human-robot collaboration, making him a noteworthy figure in intelligent robotics research.