Vikas Dhiman

About

Vikas Dhiman is a robotics and control systems researcher whose work spans safe control synthesis, autonomous navigation, simultaneous localization and mapping (SLAM), and reinforcement learning. His most influential contribution addresses the critical challenge of safe control under uncertainty, where he developed novel probabilistic and robust formulations of control Lyapunov functions and control barrier functions — work that has garnered 26 citations since 2022 and advances the theoretical foundations for deploying autonomous systems in unpredictable real-world environments. Early in his career, Dhiman tackled fundamental perception problems in robotics, including mutual camera localization and efficient point cloud surface reconstruction, demonstrating a strong grounding in mobile robotics and 3D sensing. His work on Floyd-Warshall Reinforcement Learning introduced an elegant approach to multi-goal tasks by leveraging past experiences, while his research on spatiotemporal articulated models pushed forward dynamic SLAM capabilities. More recently, Dhiman has explored inverse reinforcement learning for navigation in partially observable environments and contributed to practical humanitarian applications, including a low-cost fall-detection robot for elderly monitoring. Collectively, his research bridges rigorous theoretical control theory with applied autonomous systems, making him a notable voice in modern robotics safety and learning research.

Research Focus

Key Achievements

Top Papers

1. 1
   Safe Control Synthesis With Uncertain Dynamics and Constraints

   26 citations · 2022
2. 2
   Mutual localization: Two camera relative 6-DOF pose estimation from reciprocal fiducial observation

   15 citations · 2013
3. 3
   Voxel planes: Rapid visualization and meshification of point cloud ensembles

   14 citations · 2013
4. 4
   Floyd-Warshall Reinforcement Learning: Learning from Past Experiences to Reach New Goals

   10 citations · 2018
5. 5
   Spatiotemporal Articulated Models for Dynamic SLAM

   9 citations · 2016
6. 6
   Learning Navigation Costs from Demonstration in Partially Observable Environments

   4 citations · 2020
7. 7
   Omobot: a low-cost mobile robot for autonomous search and fall detection

   2 citations · 2024
8. 8
   Heterogeneous Multi-robot Adversarial Patrolling Using Polymatrix Games

   2 citations · 2022
9. 9
   Learning Navigation Costs from Demonstration with Semantic Observations

   2 citations · 2020

Key Collaborators

Contact & Links