Lab for Learning and Planning in Robotics

@ Interdisciplinary Science and Engineering Complex (ISEC),
Northeastern University

With the prevalence of AI and robotics, autonomous systems are very common in all aspects of life. Real-world autonomous systems must deal with noisy and limited sensors, termed partial observability, as well as potentially other agents that are also present (e.g., other robots or autonomous cars), termed multi-agent systems. We work on planning and reinforcement learning methods for dealing with these realistic partial observable and/or multi-agent settings. The resulting method will allow agents to reason about, coordinate and learn to act even in settings with limited sensing and communication.

Team

Christopher Amato
Assistant Professor

Tags:
  • multi-agent
  • partially-observable
  • reinforcement-learning
  • planning
  • robotics

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Roi Yehoshua
Assistant Teaching Professor

Tags:
  • deep
  • multi-agent
  • reinforcement-learning
  • robotics

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Enrico Marchesini
Postdoc

Tags:
  • multi-agent
  • reinforcement learning
  • evolutionary algorithms
  • safety

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Andrea Baisero
PhD Student

Tags:
  • offline-training
  • model-free
  • partially-observable
  • reinforcement-learning

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Chengguang Xu
PhD Student

Tags:
  • deep
  • hierarchical
  • reinforcement-learning
  • navigation

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Hai Nguyen
PhD Student

Tags:
  • deep
  • partially-observable
  • reinforcement-learning
  • robotics

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Xueguang Lyu
PhD Student

Tags:
  • multi-agent
  • reinforcement-learning

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Daniel Melcer
PhD Student

Tags:
  • deep
  • multi-agent
  • reinforcement-learning
  • formal verification

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Chulabhaya (CK) Wijesundara
PhD Student

Tags:
  • offline-training
  • deep
  • partially-observable
  • reinforcement-learning

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Rupali Bhati
PhD Student

Tags:
  • multi-agent
  • reinforcement learning
  • game theory

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Ethan Rathbun
PhD Student

Tags:
  • multi-agent
  • adversarial
  • reinforcement learning
  • security

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Wo Wei (Willy) Lin
PhD Student

Tags:

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Atharva Wandile
MSc Student

Tags:
  • hierarchical
  • multi-agent
  • reinforcement-learning

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Alumni

Kevin Esslinger

MSc Student
Graduated in 2023
Software Engineer @ Flow Traders
Tags:
  • deep
  • partially-observable
  • reinforcement-learning

Sites:

Sammie Katt

PhD Student
Graduated in 2023
Postdoc @ Aalto University
Tags:
  • bayesian
  • model-based
  • partially-observable
  • reinforcement-learning

Sites:

Eric Grimaldi

MSc Student
Graduated in 2022

Chip Kirchner

MSc Student
Graduated in 2022
Software Engineer @ Flexcar
Tags:
  • deep
  • multi-agent
  • reinforcement learning

David Slayback

MSc Student
Graduated in 2022
ML Applied Scientist @ OfferFit.ai
Tags:
  • hierarchical
  • reinforcement-learning

Sites:

Yuchen Xiao

PhD Student
Graduated in 2022
AI Research Scientist @ J.P.Morgan
Tags:
  • deep
  • hierarchical
  • multi-agent
  • reinforcement-learning
  • robotics

Sites:

Brett Daley

MSc Student
Graduated in 2022
PhD Student @ University of Alberta
Tags:
  • deep
  • reinforcement-learning
  • optimization

Sites:

Shuo Jiang

PhD Student
PhD Student @ GRAIL, Northeastern
Tags:
  • deep
  • multi-agent
  • reinforcement-learning
  • robotics

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Joshua Hoffman

Undergraduate
Graduated in 2022
PhD Student @ UT Austin
Tags:
  • neurosymbolic
  • reinforcement-learning
  • robotics

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Piyush Shrivastava

MSc Student

Aditya Narayanaswamy

MSc Student

Tian Xia

Undergraduate

Shengjian Chen

Undergraduate

Kevin Luo

Undergraduate