4 citations · 7 across the 7 of their papers we have counts for
8 papers
Learning from Imperfect Demonstrations via Temporal Behavior Tree-Guided Trajectory Repair
Aniruddh G. Puranic, Sebastian Schirmer, John S. Baras +1
Learning robot control policies from demonstrations is a powerful paradigm, yet real-world data is often suboptimal, noisy, or otherwise imperfect, posing significant challenges fo…
Polynomial Surrogate Training for Differentiable Ternary Logic Gate Networks
Sai Sandeep Damera, Ryan Matheu, Aniruddh G. Puranic +1
Differentiable logic gate networks (DLGNs) learn compact, interpretable Boolean circuits via gradient-based training, but all existing variants are restricted to the 16 two-input b…
Safety-Aware Reinforcement Learning for Control via Risk-Sensitive Action-Value Iteration and Quantile Regression
Clinton Enwerem, Aniruddh G. Puranic, John S. Baras +1
Mainstream approximate action-value iteration reinforcement learning (RL) algorithms suffer from overestimation bias, leading to suboptimal policies in high-variance stochastic env…
Signal Temporal Logic-Guided Apprenticeship Learning
Aniruddh G. Puranic, Jyotirmoy V. Deshmukh, Stefanos Nikolaidis
Apprenticeship learning crucially depends on effectively learning rewards, and hence control policies from user demonstrations. Of particular difficulty is the setting where the de…
Learning Performance Graphs from Demonstrations via Task-Based Evaluations
Aniruddh G. Puranic, Jyotirmoy V. Deshmukh, Stefanos Nikolaidis
In the learning from demonstration (LfD) paradigm, understanding and evaluating the demonstrated behaviors plays a critical role in extracting control policies for robots. Without…
Learning from Demonstrations using Signal Temporal Logic
Aniruddh G. Puranic, Jyotirmoy V. Deshmukh, Stefanos Nikolaidis
Learning-from-demonstrations is an emerging paradigm to obtain effective robot control policies for complex tasks via reinforcement learning without the need to explicitly design r…
Mining Environment Assumptions for Cyber-Physical System Models
Sara Mohammadinejad, Jyotirmoy V. Deshmukh, Aniruddh G. Puranic
Many complex cyber-physical systems can be modeled as heterogeneous components interacting with each other in real-time. We assume that the correctness of each component can be spe…
Interpretable Classification of Time-Series Data using Efficient Enumerative Techniques
Sara Mohammadinejad, Jyotirmoy V. Deshmukh, Aniruddh G. Puranic +2
Cyber-physical system applications such as autonomous vehicles, wearable devices, and avionic systems generate a large volume of time-series data. Designers often look for tools to…