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Our research focuses on autonomous agents and intelligent robots that plan and act under uncertainty to accomplish complex tasks. We are particularly interested in aspects relating to reliability and generalizability of methods for computing the behavior of autonomous agents, going from theoretical formulations to executable systems. Our methods draw upon formal foundations of mathematical logic, probability theory, machine learning, and well-founded notions of state and action abstractions.

Research

Generalized Planning

"Be wise, generalize!"

Planning is well known to be a hard problem. We are developing methods for acquiring useful knowledge while computing plans for small problem instances. This knowledge is then used to aid planning in larger, more difficult problems.

Often, our approaches can extract algorithmic, generalized plans that solve efficiently large classes of similar problems as well as problems with uncertainty in the quantities of objects that the agent needs to work with. The generalized plans we compute are easier to understand and are generated with proofs of correctness.

PR2 does the laundry using a generalized planner with our integrated task and motion planning system.
Papers:

Planning and Reasoning Under Uncertainty

A real robot never has perfect sensors or actuators. Instead, an intelligent robot needs to be able to solve the tasks assigned to it while handling uncertainty about the environment as well as about the effects of its own actions. This is a challenging computational problem, but also one that humans solve on a routine basis (we don't have perfect sensors or actuators either!).

We are developing new methods for efficiently expressing and solving problems where the agent has limited, incomplete information about the quantities and identities of the objects that it may encounter.

POMDP Example
Open-universe tiger POMDP with an unknown number of moving tigers.
Papers:

JEDAI: An Educational System for AI Planning and Reasoning

The objective of this project is to introduce AI planning concepts using mobile manipulator robots. It uses a visual programming interface to make these concepts easier to grasp. Users can get the robot to accomplish desired tasks by dynamically populating puzzle shaped blocks encoding the robot's possible actions. This allows users to carry out navigation, planning and manipulation by connecting blocks instead of writing code. AI explanation techniques are used to inform a user if their plan to achieve a particular goal fails. This helps them better grasp the fundamentals of AI planning.

Get JEDAI

The JEDAI system (JEDAI Explains Decision-Making Artificial Intelligence) in action.
Papers:

Autonomous Agents That Are Easy to Understand and Safe to Work With

How would a non-expert assess what their AI system can or can’t do safely? Today’s AI systems require experts to evaluate them, which limits the deployability and safe usability of AI systems.

We are developing approaches for autonomous, user-driven assessment of the capabilities of black-box taskable AI systems, even as the AI systems learn and adapt. These methods would enable users to continually evaluate and understand their AI systems in their own idiosyncratic deployments. They would prevent performance failures and accidents that can arise when AI systems are used beyond their dynamic envelopes of safe applicability. We are also designing approaches for computing user-aligned explanations of AI behavior. Together, these approaches improve the safety and usability of AI systems and enable autonomous, on-the-fly training paradigms for AI systems.

Capability Estimation
Examples of capability estimation and explanation.
Papers:

Synthesis and Analysis of Abstractions for Autonomy

In order to solve complex, long-horizon tasks such as doing the laundry, a robot needs to compute high-level strategies (e.g., would it be useful to put all the dirty clothes in a basket first?) as well as the joint movements that it should execute. Unfortunately, approaches for high-level planning rely on task-planning abstractions that are lossy and can produce “solutions” that have no feasible executions.

We are developing new methods for computing safe task-planning abstractions and for dynamically refining the task-planning abstraction to produce combined task and motion plans that are guaranteed to be executable. We are also working on utilizing abstractions in sequential decision making (SDM) for evaluating the effect of abstractions on models for SDM, as well as to search for abstractions that would aid in solving a given SDM problem.

Maze Abstraction into Rooms
Critical region (green) labelling for one sample environment. These regions can be used as waypoint abstractions.
YuMi robot builds a 3π structure with Keva planks using our STAMP algorithm.
Papers:

News

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People



Siddharth Srivastava

Siddharth Srivastava

Associate Professor
Director, AAIR Lab




Daniel Bramblett

Daniel Bramblett

Ph.D. Student

Mehdi Dadvar

Mehdi Dadvar

Ph.D. Student

Rushang Karia

Rushang Karia

Ph.D. Student




. Jayesh Nagpal

Jayesh Nagpal

Ph.D. Student

Rashmeet K Nayyar

Rashmeet K Nayyar

Ph.D. Student

Hanli Zhang

Hanli Zhang

Ph.D. Student




Alfred

Alfred

Robot

HoShi-R

HoShi-R

Robot

YuMi

YuMi

Robot




Alumni


Name Course Graduated Current Position
Pulkit Verma Ph.D. May 2024
Naman Shah Ph.D. May 2024 Postdoc at Brown University
Shivanshu Verma M.S. May 2024
Daksh Dobhal M.S. May 2024 Analyst at Goldman Sachs
Gaurav Vipat M.S. Dec 2023
Dylan Fulop B.S. Dec 2022 Software Engineer, Iridium
Kyle Joseph Atkinson B.S. Jul 2022 Software Engineer, RTX
Kiran Prasad M.S. Jul 2022 Software Developer, Amazon Robotics
Shashank Rao Marpally M.S. May 2021 PhD Student, National University of Singapore
Deepak Kala Vasudevan M.S. Dec 2020 Software Developer, Amazon Robotics
Abhyudaya Srinet M.S. Aug 2020 CTO, MentR-Me and MiM-Essay.com
Kislay Kumar M.S. Dec 2019 Software Developer, Amazon Robotics AI
Chirav Dave M.S. Dec 2019 Technical Lead, A10 Networks, Inc.
Daniel Molina M.S. May 2019 Machine Learning Engineer, State Farm
Midhun P. M. M.C.S. Dec 2018 Software Engineer, Labelbox, Inc.
Julia Nakhleh B.S. May 2019 Graduate Student, University of Wisconsin-Madison
Ryan Christensen B.S. May 2018 Data Engineer, Tellic
Perry Wang B.S. May 2018

Spring 2024

Older Group Photos

Publications


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  • Learning AI-System Capabilities under Stochasticity.
    Pulkit Verma*, Rushang Karia*, Gaurav Vipat, Anmol Gupta, Siddharth Srivastava.
    NeurIPS 2023 Workshop on Generalization in Planning, 2023.
    AI Assessment State/Action Abstractions Assistive Planning Generalized Planning
  • Learning and Using Abstractions for Robot Planning.
    Naman Shah, Abhyudaya Srinet, Siddharth Srivastava.
    ICAPS 2021 Workshop on Planning and Robotics, 2021.
    State/Action Abstractions Learning Plan Generalization and Transfer Mobile Manipulation Neuro-Symbolic AI
  • Automated Physics-based Detection and Identification of Intergalactic Clouds using Probabilistic Programming. [Poster] [Winner of Chambliss Medal]
    Rashmeet Kaur Nayyar, Mansi Padave, Sanchayeeta Borthakur, Siddharth Srivastava.
    In 234th Meeting of American Astronomical Society. Bulletin of the AAS, Vol. 51, No. 4, id. 203.05, 2019.
    Probabilistic Inference Partial Observability
  • Platform-Independent Benchmarks for Task and Motion Planning.
    Fabien Lagriffoul, Neil T. Dantam, Caelan Garrett, Aliakbar Akbari, Siddharth Srivastava, Lydia E. Kavraki.
    IEEE Robotics and Automation Letters (RA-L), Vol. 3, Issue 4, pp. 3765-3772, 2018.
    Mobile Manipulation State/Action Abstractions
  • Tractability of Planning with Loops.
    Siddharth Srivastava, Shlomo Zilberstein, Abhishek Gupta, Pieter Abbeel, Stuart Russell.
    In Proceedings of AAAI, 2015.
    Partial Observability Learning State/Action Abstractions Generalized Planning Plan Generalization and Transfer
  • First-Order Open-Universe POMDPs.
    Siddharth Srivastava, Stuart Russell, Paul Ruan, Xiang Cheng.
    In Proceedings of UAI, 2014.
    Partial Observability Probabilistic Inference
  • Qualitative Numeric Planning.
    Siddharth Srivastava, Shlomo Zilberstein, Neil Immerman, Hector Geffner.
    In Proceedings of AAAI, 2011.
    Partial Observability Generalized Planning State/Action Abstractions Plan Generalization and Transfer
  • Foundations and Applications of Generalized Planning.
    Siddharth Srivastava.
    AI Communications, Vol. 24, pp. 349-351, 2011.
    This article summarizes the contributions of Dr. Srivastava's thesis.
    Plan Generalization and Transfer Generalized Planning State/Action Abstractions
  • Finding Plans with Branches, Loops and Preconditions.
    Siddharth Srivastava, Neil Immerman, Shlomo Zilberstein.
    ICAPS 2009 Workshop on Verification and Validation of Planning and Scheduling Systems, 2009. [Slides]
    Generalized Planning State/Action Abstractions Plan Generalization and Transfer
  • Challenges in Finding Generalized Plans.
    Siddharth Srivastava, Neil Immerman, Shlomo Zilberstein.
    ICAPS 2009 Workshop on Generalized Planning: Macros, Loops, Domain Control, 2009. [Slides]
    Generalized Planning State/Action Abstractions Plan Generalization and Transfer

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