Carl Saldanha
Papers
3
Total Citations
108
H-Index
3
About
Carl Saldanha is a robotics researcher whose work centers on advancing human-robot interaction, particularly in the domain of remote teleoperation and adaptive task learning. His major contributions focus on improving the robustness and intuitiveness of high-degree-of-freedom manipulator control, a critical challenge for applications ranging from hazardous environment operations to assistive robotics. Saldanha’s most cited paper, "A Comparison of Remote Robot Teleoperation Interfaces for General Object Manipulation" (2017, 61 citations), systematically evaluates interface designs to enhance operator performance. He further refines this work in "Leveraging depth data in remote robot teleoperation interfaces for general object manipulation" (2019, 40 citations), demonstrating how depth sensing can significantly improve manipulation accuracy. In a more conceptual vein, his paper "Leveraging Large-Scale Semantic Networks for Adaptive Robot Task Learning and Execution" (2016) explores how commonsense knowledge bases can enable robots to autonomously adapt tasks—such as substituting objects—reducing the need for explicit programming. With a total of over 100 citations across his key works, Saldanha’s research bridges practical interface design and cognitive robotics, offering scalable solutions for real-world robot deployment.
Research Focus
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Top Papers
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