Pragathi Praveena
Papers
13
Total Citations
98
H-Index
6
About
Pragathi Praveena is a human-robot interaction researcher whose work sits at the intersection of robot teleoperation, motion generation, and collaborative robotics. Her research addresses fundamental challenges in making robots more intuitive and effective for human operators, spanning sensory feedback design, control frameworks, and failure recovery. Among her most influential contributions is her investigation into visually-simulated haptic feedback, where she demonstrated that visuo-proprioceptive conflicts can meaningfully communicate weight perception during remote robot operation — a creative solution to a persistent challenge in teleoperation. Her work characterizing input methods for human-to-robot demonstrations has helped establish a rigorous design vocabulary for an underexplored area critical to robot learning. On the motion planning side, her RangedIK system advances real-time robot motion generation by elegantly handling tasks with flexible goal specifications. Praveena has also made significant strides in remote collaboration, developing systems like Periscope and exploring drone-based automated viewpoints to reduce operator burden during telemanipulation. Her more recent REX framework, leveraging large language models for robot failure repair and explanation, reflects a forward-looking engagement with emerging AI capabilities. With over 90 cumulative citations across a focused body of work, Praveena's research meaningfully advances how humans and robots work together effectively.
Research Focus
Key Achievements
Top Papers
- 1
- 2Characterizing Input Methods for Human-to-Robot Demonstrations13 citations · 2019
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- 5User-Guided Offline Synthesis of Robot Arm Motion from 6-DoF Paths10 citations · 2019
- 6
- 7Understanding Control Frames in Multi-Camera Robot Telemanipulation6 citations · 2022
- 8
- 9A Design Space of Control Coordinate Systems in Telemanipulation5 citations · 2024
- 10