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Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding
Ruipeng Zhang · Chenning Yu · Jingkai Chen · Chuchu Fan · Sicun Gao

Thu Dec 01 09:00 AM -- 11:00 AM (PST) @ Hall J #800

Learning-based methods have shown promising performance for accelerating motion planning, but mostly in the setting of static environments. For the more challenging problem of planning in dynamic environments, such as multi-arm assembly tasks and human-robot interaction, motion planners need to consider the trajectories of the dynamic obstacles and reason about temporal-spatial interactions in very large state spaces. We propose a GNN-based approach that uses temporal encoding and imitation learning with data aggregation for learning both the embeddings and the edge prioritization policies. Experiments show that the proposed methods can significantly accelerate online planning over state-of-the-art complete dynamic planning algorithms. The learned models can often reduce costly collision checking operations by more than 1000x, and thus accelerating planning by up to 95%, while achieving high success rates on hard instances as well.

Author Information

Ruipeng Zhang (University of California, San Diego)
Chenning Yu (UC San Diego)
Jingkai Chen (Amazon Robotics)
Chuchu Fan (Massachusetts Institute of Technology)
Chuchu Fan

Bio: Chuchu Fan an Assistant Professor in the Department of Aeronautics and Astronautics at MIT. Before that, she was a postdoc researcher at Caltech and got her Ph.D. from the Electrical and Computer Engineering Department at the University of Illinois at Urbana-Champaign in 2019. She earned her bachelor’s degree from Tsinghua University, Department of Automation. Her group at MIT works on using rigorous mathematics including formal methods, machine learning, and control theory for the design, analysis, and verification of safe autonomous systems. Chuchu’s dissertation work “Formal methods for safe autonomy” won the ACM Doctoral Dissertation Award in 2020.

Sicun Gao (University of California, San Diego)

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