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

Sites:

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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Sammie Katt

PhD Student

Tags:
  • bayesian
  • model-based
  • partially-observable
  • reinforcement-learning

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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

Sites:

Xueguang Lyu

PhD Student

Tags:
  • multi-agent
  • reinforcement-learning

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Kevin Esslinger

MSc Student

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

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Daniel Melcer

PhD Student

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

Sites:

Chulabhaya Wijesundara

PhD Student

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

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Atharva Wandile

MSc Student

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

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Alumni

Eric Grimaldi

MSc Student
Graduated in 2022

Chip Kirchner

Co-op Student for DARPA Project

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

Sites:

Joshua Hoffman

Undergraduate
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