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Robot Learning

CLOUD: Contrastive Learning of Unsupervised Dynamics
Jianren Wang*, Yujie Lu*, Hang Zhao (* indicates equal contribution)
2020 Conference on Robot Learning
[Project Page] [Code] [Abstract] [Bibtex]

Developing agents that can perform complex control tasks from high dimensional observations such as pixels is challenging due to difficulties in learning dynamics efficiently. In this work, we propose to learn forward and inverse dynamics in a fully unsupervised manner via contrastive estimation. Specifically, we train a forward dynamics model and an inverse dynamics model in the feature space of states and actions with data collected from random exploration. Unlike most existing deterministic models, our energy-based model takes into account the stochastic nature of agent-environment interactions. We demonstrate the efficacy of our approach across a variety of tasks including goal-directed planning and imitation from observations.

    Author = {Wang, Jianren and Lu, Yujie and Zhao, Hang},
    Title = {CLOUD: Contrastive Learning of Unsupervised Dynamics},
    Booktitle = {CORL},
    Year = {2020}
Integration of a Low-Cost Three-Axis Sensor for Robot Force Control
Shuyang Chen, Jianren Wang, Peter Kazanzides
2018 Second IEEE International Conference on Robotic Computing