Daniel Rakita

Daniel studies the correspondence between human motions and actions and robot motions and actions. For instance, he has developed a telemanipulation method that maps human arm motions to robot arm motions in real-time to afford intuitive control of robot arms using a novel motion synthesis algorithm called RelaxedIK, an automatic dynamic camera method that continuously optimizes a viewpoint for a remote user, and a robot bimanual shared-control method inspired by how people naturally perform bimanual manipulations.
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Rakita, D., B. Mutlu, and M. Gleicher. “An Analysis of RelaxedIK: An Optimization-Based Framework for Generating Accurate and Feasible Robot Arm motions”. Autonomous Robots, Vol. 44, no. 7, Springer US, pp. 1341-58.
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Bodden, C., D. Rakita, B. Mutlu, and M. Gleicher. “A Flexible Optimization-Based Method for Synthesizing Intent-Expressive Robot Arm motion”. The International Journal of Robotics Research, Vol. 37, no. 11, 2018, pp. 1376-94.
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Praveena, P., D. Rakita, B. Mutlu, and M. Gleicher. “User-Guided Offline Synthesis of Robot Arm Motion from 6-DoF Paths”. 2019 International Conference on Robotics and Automation (ICRA), IEEE, 2019, pp. 8825-31.
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Praveena, P., D. Rakita, B. Mutlu, and M. Gleicher. “Supporting Perception of Weight through Motion-Induced Sensory Conflicts in Robot Teleoperation”. Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, 2020, pp. 509-17.
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Rakita, D., B. Mutlu, and M. Gleicher. “Effects of Onset Latency and Robot Speed Delays on Mimicry-Control Teleoperation”. HRI, 2020, pp. 519-27.
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Schoen, A., D. Sullivan, H. Zhang, D. Rakita, and B. Mutlu. Lively: Enabling Multimodal, Lifelike, and Extensible Real-Time Robot Motion. Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’23), 2023.