Hi! I am a PhD Candidate in the Robotics Institute at Carnegie Mellon University advised by Prof. Maxim Likhachev. I work on solving challenging non-prehensile manipulation planning problems in cluttered scenes where the robot may need to rearrange some objects in order to access others. My research relies on a key insight that abstracts this problem of manipulation among movable objects to multi-agent pathfinding.

Before my PhD, I obtained a Masters in Robotics degree, also from CMU RI. I worked with Prof. Martial Hebert on learning recovery maneuevers for perception system failures during monocular quadrotor flight in dense forests (thesis pdf, url).

You can email me at dhruvmsaxena [at] gmail [dot] com.

News

Publications

1. Planning for Manipulation Among Movable Objects: Deciding Which Objects Go Where, In What Order, And How
   Dhruv Mauria Saxena, Maxim Likhachev
   Accepted to the International Conference on Automated Planning and Scheduling (ICAPS), 2023
   [pdf]

2. Planning for Complex Non-prehensile Manipulation Among Movable Objects by Interleaving Multi-Agent Pathfinding and Physics-Based Simulation
   Dhruv Mauria Saxena, Maxim Likhachev
   Accepted to the IEEE International Conference on Robotics and Automation (ICRA), 2023
   [pdf]

3. Human-Scale Mobile Manipulation Using RoMan
   Chad Kessens et al.
   Field Robotics; Special Issue on Robotics Collaborative Technology Alliance (RCTA) Program
   [pdf]

4. AMRA*: Anytime Multi-Resolution Multi-Heuristic A*
   Dhruv Mauria Saxena, Tushar Kusnur, Maxim Likhachev
   IEEE International Conference on Robotics and Automation (ICRA), 2022
   [bibtex] [pdf]

5. Manipulation Planning Among Movable Obstacles Using Physics-Based Adaptive Motion Primitives
   Dhruv Mauria Saxena, Muhammad Suhail Saleem, Maxim Likhachev
   IEEE International Conference on Robotics and Automation (ICRA), 2021
   [bibtex] [pdf]

6. Search-based Planning for Active Sensing in Goal-Directed Coverage Tasks
   Tushar Kusnur, Dhruv Mauria Saxena, Maxim Likhachev
   IEEE International Conference on Robotics and Automation (ICRA), 2021
   [bibtex] [pdf]

7. Driving in Dense Traffic with Model-Free Reinforcement Learning
   Dhruv Mauria Saxena, Sangjae Bae, Alireza Nakhaei, Kikuo Fujimura, Maxim Likhachev
   IEEE International Conference on Robotics and Automation (ICRA), 2020
   [bibtex] [pdf]

8. Cooperation-Aware Lane Change Maneuver in Dense Traffic based on Model Predictive Control with Recurrent Neural Network
   Sangjae Bae, Dhruv Mauria Saxena, Alireza Nakhaei, Chiho Choi, Kikuo Fujimura, Scott Moura
   IEEE American Control Conference (ACC), 2020
   [bibtex] [pdf]

9. Bidirectional Heuristic Search for Motion Planning with an Extend Operator
   Allen Cheng, Dhruv Mauria Saxena, Maxim Likhachev
   IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019
   [bibtex] [pdf]

10. A Planning Framework for Persistent, Multi-UAV Coverage with Global Deconfliction
    Tushar Kusnur, Shohin Mukherjee, Dhruv Mauria Saxena, Tomoya Fukami, Takayuki Koyama, Oren Salzman, Maxim Likhachev
    Springer Field and Service Robotics (FSR), 2019
    [bibtex] [pdf]

11. An experiment to evaluate robotic grasping of occluded objects
    Arnon Hurwitz, Marshal Childers, Andrew Dornbush, Dhruv Mauria Saxena, Maxim Likhachev, Craig Lennon
    SPIE Unmanned Systems Technology XX, 2018
    [bibtex] [pdf]

12. Learning Robust Failure Response for Autonomous Vision Based Flight
    Dhruv Mauria Saxena, Vince Kurtz, Martial Hebert
    IEEE International Conference on Robotics and Automation (ICRA), 2017
    [bibtex] [pdf]

Workshop Papers

1. Imagine All Objects Are Robots: A Multi-Agent Pathfinding Perspective on Manipulation Among Movable Objects
   Dhruv Mauria Saxena, Maxim Likhachev
   Workshop on Multi-Agent Path Finding at 37th AAAI Conference on Artificial Intelligence (AAAI-23), 2023
   [bibtex] [pdf]

2. Learning Contextual Actions for Heuristic Search-Based Motion Planning
   Dhruv Mauria Saxena, Maxim Likhachev
   Fourth Machine Learning in Planning and Control of Robot Motion Workshop at IEEE International Conference on Robotics and Automation (ICRA), 2020
   [bibtex] [pdf]

3. Deep Flight: Autonomous Quadrotor Navigation with Deep Reinforcement Learning
   Ratnesh Madaan, Rogerio Bonatti, Dhruv Mauria Saxena, Sebastian Scherer
   Workshop on Learning Perception and Control for Autonomous Flight: Safety, Memory, and Efficiency at Robotics: Science and Systems (RSS), 2017
   [bibtex] [pdf]