Tirthankar Bandyopadhyay

About

Tirthankar Bandyopadhyay is a robotics researcher whose work spans motion planning under uncertainty, autonomous legged locomotion, and robot perception — areas in which he has made lasting and wide-ranging contributions. His early research tackled the challenge of tracking unpredictable targets among obstacles, culminating in foundational work on greedy tracking strategies and 3D target tracking that drew on visibility and risk modeling. His 2013 paper on intention-aware motion planning, now with 189 citations, established him as a key voice in planning frameworks that account for the intentions of other agents — a cornerstone challenge in human-robot interaction and autonomous navigation. Bandyopadhyay has also advanced motion planning under compounded uncertainty, addressing imprecise sensing, motion, and environmental maps in real-world deployments. More recently, his focus has shifted toward legged robotics, where he co-developed Magneto, an innovative quadruped climbing robot, and pioneered perceptive whole-body planning methods that enable multi-legged robots to navigate confined and cluttered spaces — work that has collectively attracted nearly 200 citations since 2017. His terrain traversability research further bridges perception and planning for robust outdoor autonomy. Across more than 600 total citations, Bandyopadhyay's career reflects a deep commitment to making robots capable, adaptive, and intelligent in complex real-world environments.

Research Focus

Key Achievements

Top Papers

1. 1
   Intention-Aware Motion Planning

   189 citations · 2013
2. 2
   Global motion planning under uncertain motion, sensing, and environment map

   72 citations · 2012
3. 3
   Magneto: A Versatile Multi-Limbed Inspection Robot

   66 citations · 2018
4. 4
   Perceptive whole‐body planning for multilegged robots in confined spaces

   63 citations · 2020
5. 5
   Walking Posture Adaptation for Legged Robot Navigation in Confined Spaces

   62 citations · 2019
6. 6
   How Rough Is the Path? Terrain Traversability Estimation for Local and Global Path Planning

   51 citations · 2022
7. 7
   A greedy strategy for tracking a locally predictable target among obstacles

   40 citations · 2006
8. 8
   Stealth Tracking of an Unpredictable Target among Obstacles

   31 citations · 2005
9. 9
   The Multilegged Autonomous eXplorer (MAX)

   23 citations · 2017
10. 10
    Motion Planning for 3-D Target Tracking among Obstacles

    20 citations · 2010

Key Collaborators

Contact & Links