Congrats to Shayegan Omidshafiei, Dong Ki Kim, Miao Liu, Gerald Tesauro, Matthew Riemer, Chris Amato, Murray Campbell for their paper Learning to Teach in Cooperative Multiagent Reinforcement Learning! They are looking forward to present and discuss the work at AAAI-19 this end of January.

System allows drones to cooperatively explore terrain under thick forest canopies where GPS signals are unreliable.

MIT News Article

ACL members (Björn Lütjens, Kris Frey, Macheng Shen, Michael Everett) have published works at IROS 2018!

Papers

• Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning
• Transferable Pedestrian Motion Prediction Models at Intersections
• Safe Reinforcement Learning with Model Uncertainty Estimates (Spotlight Presentation in Workshop on Machine Learning in Robot Motion Planning)
• Planning Beyond The Sensing Horizon Using a Learned Context (Workshop on Machine Learning in Robot Motion Planning)

Congratulations to Shayegan Omidshafiei who has finished his PhD in Decentralized Teaching and Learning in Cooperative Multiagent Systems at ACL. Thanks for all the helpful advice to the first-years and best of luck at Google DeepMind in Paris!

Shayegan’s Homepage, Shayegan’s Thesis

ACL paper Talk Resource-Efficiently to Me: Optimal Communication Planning for Distributed Loop Closure Detection by Matthew Giamou, Kasra Khosoussi and Jonathan P. How was a finalist for the ICRA Best Multi-Robot Systems Paper Award 2018.

Awards page, Nominated paper

Congratulations to ACL alumnus Trevor Campbell (PhD 2016) on accepting a faculty position at University of British Columbia Department of Statistics!

Trevor’s Homepage, UBC Statistics