143 citations · 440 across the 42 of their papers we have counts for
7 papers · 1 filter
Learning adaptive planning representations with natural language guidance
Lionel Wong, Jiayuan Mao, Pratyusha Sharma +5
Effective planning in the real world requires not only world knowledge, but the ability to leverage that knowledge to build the right representation of the task at hand. Decades of…
HandMeThat: Human-Robot Communication in Physical and Social Environments
Yanming Wan, Jiayuan Mao, Joshua B. Tenenbaum
We introduce HandMeThat, a benchmark for a holistic evaluation of instruction understanding and following in physical and social environments. While previous datasets primarily foc…
CLEVRER-Humans: Describing Physical and Causal Events the Human Way
Jiayuan Mao, Xuelin Yang, Xikun Zhang +2
Building machines that can reason about physical events and their causal relationships is crucial for flexible interaction with the physical world. However, most existing physical…
Programmatically Grounded, Compositionally Generalizable Robotic Manipulation
Renhao Wang, Jiayuan Mao, Joy Hsu +3
Robots operating in the real world require both rich manipulation skills as well as the ability to semantically reason about when to apply those skills. Towards this goal, recent w…
Learning Rational Subgoals from Demonstrations and Instructions
Zhezheng Luo, Jiayuan Mao, Jiajun Wu +3
We present a framework for learning useful subgoals that support efficient long-term planning to achieve novel goals. At the core of our framework is a collection of rational subgo…
PDSketch: Integrated Planning Domain Programming and Learning
Jiayuan Mao, Tomás Lozano-Pérez, Joshua B. Tenenbaum +1
This paper studies a model learning and online planning approach towards building flexible and general robots. Specifically, we investigate how to exploit the locality and sparsity…
Neural Logic Machines
Honghua Dong, Jiayuan Mao, Tian Lin +3
We propose the Neural Logic Machine (NLM), a neural-symbolic architecture for both inductive learning and logic reasoning. NLMs exploit the power of both neural networks---as funct…