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About

Cihan Acar is a robotics researcher whose work sits at the intersection of perception, manipulation, and locomotion, with a focus on enabling robots to operate autonomously in complex, unstructured environments. His key research areas include visuo-tactile feedback for object manipulation, state estimation for hybrid wheeled-legged robots, and learning-based motion planning under constraints. Acar’s most cited work, “Visuo-Tactile Feedback-Based Robot Manipulation for Object Packing” (2023, 24 citations), addresses the challenge of perceptual uncertainty by fusing visual and tactile sensing to improve robotic packing tasks. He also developed a novel state estimation framework for hybrid wheeled-legged robots performing mobile manipulation (2021, 12 citations), introducing a unified odometry approach that seamlessly tracks locomotion. His contributions to constrained motion planning using generative models (2021, 10 citations) and 6D pose estimation with correlation fusion (2021, 10 citations) further demonstrate his impact on robust robotic perception and planning. Acar’s work on supervised autonomy for remote teleoperation of quadrupedal bimanual manipulators (2021, 6 citations) highlights his commitment to practical, deployable systems. With a growing citation record and a focus on multi-modal sensing and learning, Acar is advancing the frontier of autonomous robot manipulation and mobility.