Aniruddh Gopinath Puranic — accepted AI-conference papers

2023

Learning Performance Graphs From Demonstrations via Task-Based Evaluations

Aniruddh Gopinath Puranic, Jyotirmoy V. Deshmukh, Stefanos Nikolaidis

RA-L 2023

In the paradigm of robot learning-from-demonstra tions (LfD), understanding and evaluating the demonstrated behaviors plays a critical role in extracting control policies for robots. Without this knowledge, a robot may infer incorrect reward functions that lead to undesirable or unsafe control polic

Cited by 5 Source ↗Scholar ↗

2021

Learning From Demonstrations Using Signal Temporal Logic in Stochastic and Continuous Domains

Aniruddh Gopinath Puranic, Jyotirmoy V. Deshmukh, Stefanos Nikolaidis

RA-L 2021

Learning control policies that are safe, robust and interpretable are prominent challenges in developing robotic systems. Learning-from-demonstrations with formal logic is an arising paradigm in reinforcement learning to estimate rewards and extract robot control policies that seek to overcome these

Cited by 33 Source ↗Scholar ↗