Papers
16
Total Citations
580
H-Index
5
About
Siddharth Srivastava is a leading researcher in robotics and AI, whose work centers on bridging the gap between high-level task planning and low-level motion control. His seminal 2014 paper on combined task and motion planning (474 citations) introduced a groundbreaking approach that uses off-the-shelf task planners with an extensible interface layer, enabling robots to reason about both discrete actions and continuous motions in complex environments. This work has become foundational in the field, influencing how robots handle long-horizon tasks like manipulation and navigation. Srivastava has also made significant contributions to explainable AI, developing systems like JEDAI that help non-experts understand robot behavior through skill-aligned explanations. His research on hierarchical planning and abstraction learning, including work using deep learning to bootstrap abstractions for reliable robot planning, addresses critical challenges in scalability and safety. More recently, he has tackled joint communication and motion planning for collaborative robots (cobots), advancing human-robot interaction. With over 560 total citations, Srivastava’s work continues to shape the future of autonomous systems, making robots more capable, transparent, and trustworthy in real-world applications.
Research Focus
Key Achievements
Top Papers
- 1
- 2Using Classical Planners for Tasks with Continuous Operators in Robotics26 citations · 2013
- 3Tractability of Planning with Loops20 citations · 2015
- 4Metaphysics of Planning Domain Descriptions16 citations · 2016
- 5
- 6Joint Communication and Motion Planning for Cobots5 citations · 2022
- 7An Anytime Algorithm for Task and Motion MDPs5 citations · 2018
- 8
- 9JEDAI: A System for Skill-Aligned Explainable Robot Planning5 citations · 2021
- 10A real-time ball trajectory follower using Robot Operating System4 citations · 2015