Sarath Sreedharan

About

Sarath Sreedharan is a prominent AI researcher whose work sits at the intersection of automated planning, human-robot interaction, and explainable artificial intelligence. His research focuses on making autonomous systems more transparent, predictable, and trustworthy when operating alongside humans — a challenge of growing importance as intelligent robots enter safety-critical environments. Sreedharan's most influential contribution is his foundational work on plan explicability and predictability for robot task planning, which has accumulated over 135 citations and established key frameworks for reducing the cognitive burden humans face when interpreting autonomous agent behavior. Building on this, his model reconciliation approach addresses how agents can explain decisions by bridging gaps between their own world models and those of human collaborators — a nuanced and practically significant advancement in human-aware planning. His research extends into mixed-reality workspaces, alternative human-robot communication modalities including augmented reality and electrophysiological monitoring, and trust-aware planning systems. His 2021 Bayesian unification of interpretability measures represents a maturing theoretical contribution, drawing together previously fragmented concepts into a coherent framework. Across his body of work, Sreedharan consistently tackles the challenge of making AI systems not merely capable, but genuinely comprehensible and cooperative partners for human users.

Research Focus

Key Achievements

Top Papers

1. 1
   Plan explicability and predictability for robot task planning

   135 citations · 2017
2. 2
   Projection-Aware Task Planning and Execution for Human-in-the-Loop Operation of Robots in a Mixed-Reality Workspace

   47 citations · 2018
3. 3
   Handling Model Uncertainty and Multiplicity in Explanations via Model Reconciliation

   39 citations · 2018
4. 4
   Alternative Modes of Interaction in Proximal Human-in-the-Loop Operation of Robots

   28 citations · 2017
5. 5
   Plan Explicability and Predictability for Robot Task Planning

   23 citations · 2015
6. 6
   Balancing Explicability and Explanations in Human-Aware Planning

   17 citations · 2019
7. 7
   Balancing Explicability and Explanation in Human-Aware Planning

   16 citations · 2017
8. 8
   Trust-Aware Planning

   12 citations · 2023
9. 9
   Explicability versus Explanations in Human-Aware Planning

   10 citations · 2018
10. 10
    A Unifying Bayesian Formulation of Measures of Interpretability in Human-AI Interaction

    8 citations · 2021

Key Collaborators

Contact & Links