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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Sammie Katt

PhD Student

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

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Yuchen Xiao

PhD Student

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

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Andrea Baisero

PhD Student

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

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David Slayback

PhD Student

Tags:
  • hierarchical
  • 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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Brett Daley

PhD Student

Tags:
  • deep
  • reinforcement-learning
  • optimization

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Kevin Esslinger

PhD Student

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

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Daniel Melcer

PhD Student

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

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Alumni

Shuo Jiang

PhD Student

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

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Joshua Hoffman

Undergraduate

Tags:
  • neurosymbolic
  • reinforcement-learning
  • robotics

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Piyush Shrivastava

MSc Student

Tags:

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Aditya Narayanaswamy

MSc Student

Tags:

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Tian Xia

Undergraduate

Tags:

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Shengjian Chen

Undergraduate

Tags:

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Kevin Luo

Undergraduate

Tags:

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