53 citations · 232 across the 44 of their papers we have counts for
52 papers
FP-IRL: Fokker--Planck Inverse Reinforcement Learning -- A Physics-Constrained Approach to Markov Decision Processes
Chengyang Huang, Siddhartha Srivastava, Kenneth K. Y. Ho +4
Inverse reinforcement learning (IRL) is a powerful paradigm for uncovering the incentive structure that drives agent behavior, by inferring an unknown reward function from observed…
Autonomous Capability Assessment of Sequential Decision-Making Systems in Stochastic Settings (Extended Version)
Pulkit Verma, Rushang Karia, Siddharth Srivastava
It is essential for users to understand what their AI systems can and can't do in order to use them safely. However, the problem of enabling users to assess AI systems with sequent…
Inference of weak-form partial differential equations describing migration and proliferation mechanisms in wound healing experiments on cancer cells
Patrick C. Kinnunen, Siddhartha Srivastava, Zhenlin Wang +7
Targeting signaling pathways that drive cancer cell migration or proliferation is a common therapeutic approach. A popular experimental technique, the scratch assay, measures the m…
Predicting Citi Bike Demand Evolution Using Dynamic Graphs
Alexander Saff, Mayur Bhandary, Siddharth Srivastava
Bike sharing systems often suffer from poor capacity management as a result of variable demand. These bike sharing systems would benefit from models to predict demand in order to m…
Hierarchical Decomposition and Analysis for Generalized Planning
Siddharth Srivastava
This paper presents new methods for analyzing and evaluating generalized plans that can solve broad classes of related planning problems. Although synthesis and learning of general…
Learning Dynamic Abstract Representations for Sample-Efficient Reinforcement Learning
Mehdi Dadvar, Rashmeet Kaur Nayyar, Siddharth Srivastava
In many real-world problems, the learning agent needs to learn a problem's abstractions and solution simultaneously. However, most such abstractions need to be designed and refined…
Multi-Task Option Learning and Discovery for Stochastic Path Planning
Naman Shah, Siddharth Srivastava
This paper addresses the problem of reliably and efficiently solving broad classes of long-horizon stochastic path planning problems. Starting with a vanilla RL formulation with a…
Relational Abstractions for Generalized Reinforcement Learning on Symbolic Problems
Rushang Karia, Siddharth Srivastava
Reinforcement learning in problems with symbolic state spaces is challenging due to the need for reasoning over long horizons. This paper presents a new approach that utilizes rela…
Learning Generalized Policy Automata for Relational Stochastic Shortest Path Problems
Rushang Karia, Rashmeet Kaur Nayyar, Siddharth Srivastava
Several goal-oriented problems in the real-world can be naturally expressed as Stochastic Shortest Path Problems (SSPs). However, the computational complexity of solving SSPs makes…
Differential Assessment of Black-Box AI Agents
Rashmeet Kaur Nayyar, Pulkit Verma, Siddharth Srivastava
Much of the research on learning symbolic models of AI agents focuses on agents with stationary models. This assumption fails to hold in settings where the agent's capabilities may…
Using Deep Learning to Bootstrap Abstractions for Hierarchical Robot Planning
Naman Shah, Siddharth Srivastava
This paper addresses the problem of learning abstractions that boost robot planning performance while providing strong guarantees of reliability. Although state-of-the-art hierarch…
Beyond Mono to Binaural: Generating Binaural Audio from Mono Audio with Depth and Cross Modal Attention
Kranti Kumar Parida, Siddharth Srivastava, Gaurav Sharma
Binaural audio gives the listener an immersive experience and can enhance augmented and virtual reality. However, recording binaural audio requires specialized setup with a dummy h…
JEDAI: A System for Skill-Aligned Explainable Robot Planning
Naman Shah, Pulkit Verma, Trevor Angle +1
This paper presents JEDAI, an AI system designed for outreach and educational efforts aimed at non-AI experts. JEDAI features a novel synthesis of research ideas from integrated ta…
Joint Communication and Motion Planning for Cobots
Mehdi Dadvar, Keyvan Majd, Elena Oikonomou +2
The increasing deployment of robots in co-working scenarios with humans has revealed complex safety and efficiency challenges in the computation robot behavior. Movement among huma…
Learning Causal Models of Autonomous Agents using Interventions
Pulkit Verma, Siddharth Srivastava
One of the several obstacles in the widespread use of AI systems is the lack of requirements of interpretability that can enable a layperson to ensure the safe and reliable behavio…
Depth Infused Binaural Audio Generation using Hierarchical Cross-Modal Attention
Kranti Kumar Parida, Siddharth Srivastava, Neeraj Matiyali +1
Binaural audio gives the listener the feeling of being in the recording place and enhances the immersive experience if coupled with AR/VR. But the problem with binaural audio recor…
An Anytime Hierarchical Approach for Stochastic Task and Motion Planning
Naman Shah, Siddharth Srivastava
In order to solve complex, long-horizon tasks, intelligent robots need to carry out high-level, abstract planning and reasoning in conjunction with motion planning. However, abstra…
Discovering User-Interpretable Capabilities of Black-Box Planning Agents
Pulkit Verma, Shashank Rao Marpally, Siddharth Srivastava
Several approaches have been developed for answering users' specific questions about AI behavior and for assessing their core functionality in terms of primitive executable actions…
Planning for Proactive Assistance in Environments with Partial Observability
Anagha Kulkarni, Siddharth Srivastava, Subbarao Kambhampati
This paper addresses the problem of synthesizing the behavior of an AI agent that provides proactive task assistance to a human in settings like factory floors where they may coexi…
Beyond Image to Depth: Improving Depth Prediction using Echoes
Kranti Kumar Parida, Siddharth Srivastava, Gaurav Sharma
We address the problem of estimating depth with multi modal audio visual data. Inspired by the ability of animals, such as bats and dolphins, to infer distance of objects with echo…
Exploiting Local Geometry for Feature and Graph Construction for Better 3D Point Cloud Processing with Graph Neural Networks
Siddharth Srivastava, Gaurav Sharma
We propose simple yet effective improvements in point representations and local neighborhood graph construction within the general framework of graph neural networks (GNNs) for 3D…
Bandgap optimization in combinatorial graphs with tailored ground states: Application in Quantum annealing
Siddhartha Srivastava, Veera Sundararaghavan
A mixed-integer linear programming (MILP) formulation is presented for parameter estimation of the Potts model. Two algorithms are developed; the first method estimates the paramet…
Learning and Using Abstractions for Robot Planning
Naman Shah, Abhyudaya Srinet, Siddharth Srivastava
Robot motion planning involves computing a sequence of valid robot configurations that take the robot from its initial state to a goal state. Solving a motion planning problem opti…
Learning Generalized Relational Heuristic Networks for Model-Agnostic Planning
Rushang Karia, Siddharth Srivastava
Computing goal-directed behavior is essential to designing efficient AI systems. Due to the computational complexity of planning, current approaches rely primarily upon hand-coded…