Papers
8
Total Citations
83
H-Index
5
About
Shiraj Sen is a pioneering researcher in autonomous robotics, focusing on the intersection of skill acquisition, hierarchical manipulation, and risk-aware navigation. His foundational work, "Intrinsically motivated hierarchical manipulation" (2008, 32 citations), introduced a groundbreaking framework that uses intrinsic reward functions to enable robots to build deep control knowledge incrementally—a key contribution to lifelong learning in robotics. Sen further advanced the field by addressing generalization and transfer in robot control (2008, 16 citations), proposing factorable control abstractions that allow learning algorithms to converge efficiently across tasks. His holistic approach to bridging autonomous skill learning with task-specific planning (2019, 10 citations) challenged the traditional separation of these domains, offering a unified representational framework. Notable achievements include the development of the Aspect Transition Graph (2015, 6 citations), an affordance-based model for structuring robot-environment interactions, and his recent work on risk-aware autonomous navigation (2021, 4 citations), which addresses critical challenges in battlefield robotics. With over 80 total citations, Sen’s research has shaped how robots learn, plan, and adapt in unstructured environments, making him a key figure in advancing autonomous systems toward robust, human-compatible operation.
Research Focus
Key Achievements
Top Papers
- 1Intrinsically motivated hierarchical manipulation32 citations · 2008
- 2Generalization and Transfer in Robot Control16 citations · 2008
- 3Bridging The Gap Between Autonomous SkillLearning And Task-Specific Planning10 citations · 2019
- 4Hierarchical skills and skill-based representation9 citations · 2011
- 5The Aspect Transition Graph: An Affordance-Based Model6 citations · 2015
- 6Risk-aware autonomous navigation4 citations · 2021
- 7Choosing informative actions for manipulation tasks3 citations · 2011
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