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
Enrico Marchesini
Postdoc
Tags:
- multi-agent
- reinforcement learning
- evolutionary algorithms
- safety
Sites:
Sammie Katt
PhD Student
Tags:
- bayesian
- model-based
- partially-observable
- reinforcement-learning
Sites:
Andrea Baisero
PhD Student
Tags:
- offline-training
- model-free
- partially-observable
- reinforcement-learning
Sites:
Chengguang Xu
PhD Student
Hai Nguyen
PhD Student
Xueguang Lyu
PhD Student
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
Sites:
Atharva Wandile
MSc Student
Alumni
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
Yuchen Xiao
PhD Student
Graduated in 2022
AI Research Scientist @ J.P.Morgan
Tags:
- deep
- hierarchical
- multi-agent
- reinforcement-learning
- robotics