Arun Lakshmanan

About

Arun Lakshmanan is a researcher at the forefront of safe autonomous systems, specializing in nonlinear control theory, contraction metrics, and human-robot interaction. His most impactful work introduces a rigorous framework for guaranteed trajectory tracking in nonlinear systems subject to external disturbances, using robust control contraction metrics (CCM) to minimize tracking error—a contribution that has earned 37 citations since 2022. Complementing this, his 2020 paper on safe feedback motion planning (34 citations) presents a planner-agnostic approach that leverages contraction theory and ℒ₁-adaptive control to certify "safe tubes" around desired trajectories, enabling robots to operate reliably despite imperfect models or disturbances. Lakshmanan also explores human-centered robotics through his "Carebots" research, which proposes using small flying robots to prolong independent living for older adults, and a virtual-reality study measuring emotional responses to multicopter interactions in residential settings. His work bridges rigorous theoretical guarantees with practical safety in human environments, making him a key contributor to the next generation of resilient, trustworthy autonomous systems for healthcare and beyond.

Research Focus

Key Achievements

Top Papers

1. 1
   Tube-Certified Trajectory Tracking for Nonlinear Systems With Robust Control Contraction Metrics

   37 citations · 2022
2. 2
   Safe Feedback Motion Planning: A Contraction Theory and ℒ₁-Adaptive Control Based Approach

   34 citations · 2020
3. 3
   Carebots

   4 citations · 2016
4. 4
   Safe Feedback Motion Planning: A Contraction Theory and $\mathcal{L}_1$-Adaptive Control Based Approach

   3 citations · 2020
5. 5
   VR study of human-multicopter interaction in a residential setting

   2 citations · 2016

Key Collaborators

Contact & Links