I am Linfeng Zhao (赵林风), a CS Ph.D. student at Khoury College of Computer Sciences of Northeastern University. I’m advised by Prof. Lawson L.S. Wong and also working with Prof. Robin Walters. During PhD: I am collaborating with and visiting MIT LIS group led by Prof. Leslie Kaelbling and Prof. Tomás Lozano-Pérez. I interned at Meta (2024 summer), Boston Dynamics AI Institute (2023 Spring-Summer) and earlier at Amazon Science (2021 Summer). During undergraduate: I interned at Microsoft Research Asia (2019) and earlier worked with Prof. Hao Su at UC San Diego (2018-19).
My research focuses on enabling robots and agents to act in open-world and long-horizon scenarios by advancing learning for planning and world modeling. I employ structured approaches that integrate planning and perception, leveraging lossless and lossy abstractions such as symmetry, compositionality, and hierarchy. My work involves building learning-based systems that bridge perception and planning through neural networks, including the use of recent pre-trained foundation models, to enhance scalability, generalizability, and efficiency in decision-making systems.
We study the problem of action sampling and propose a method to incorporate equivariance properties to the action sampling procedure.
This work learns to navigate end-to-end with map input, aiming to generalize to novel map layouts in zero-shot.
We enable a robot to rapidly and autonomously specialize parameterized skills by planning to practice them. The robot decides what skills to practice and how to practice them. The robot is left alone for hours, repeatedly practicing and improving.
We study E(2) Euclidean equivariance in navigation on geometric graphs and develop message passing network to solve it.
We formulate how differentiable planning algorithms can exploit inherent symmetry in path planning problems, named SymPlan, and propose practical algorithms.
We study how implicit differentiation helps scale up and improve convergence of differentiable planning algorithms.
We formulate compositional generalization in object-oriented world modeling, and propose a soft and efficient mechanism for practice.
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