no code implementations • 1 Oct 2022 • Zheng Wu, Yichen Xie, Wenzhao Lian, Changhao Wang, Yanjiang Guo, Jianyu Chen, Stefan Schaal, Masayoshi Tomizuka
Experimental results demonstrate that our proposed method achieves policy generalization to unseen compositional tasks in a zero-shot manner.
2 code implementations • 30 Jan 2022 • Bowen Wen, Wenzhao Lian, Kostas Bekris, Stefan Schaal
The canonical object representation is learned solely in simulation and then used to parse a category-level, task trajectory from a single demonstration video.
1 code implementation • 19 Sep 2021 • Bowen Wen, Wenzhao Lian, Kostas Bekris, Stefan Schaal
This work proposes a framework to learn task-relevant grasping for industrial objects without the need of time-consuming real-world data collection or manual annotation.
no code implementations • 21 Mar 2021 • Jianlan Luo, Oleg Sushkov, Rugile Pevceviciute, Wenzhao Lian, Chang Su, Mel Vecerik, Ning Ye, Stefan Schaal, Jon Scholz
In this paper we define criteria for industry-oriented DRL, and perform a thorough comparison according to these criteria of one family of learning approaches, DRL from demonstration, against a professional industrial integrator on the recently established NIST assembly benchmark.
no code implementations • 8 Mar 2021 • Wenzhao Lian, Tim Kelch, Dirk Holz, Adam Norton, Stefan Schaal
In recent years, many learning based approaches have been studied to realize robotic manipulation and assembly tasks, often including vision and force/tactile feedback.
1 code implementation • 4 Jul 2015 • Makoto Yamada, Wenzhao Lian, Amit Goyal, Jianhui Chen, Kishan Wimalawarne, Suleiman A. Khan, Samuel Kaski, Hiroshi Mamitsuka, Yi Chang
We propose the convex factorization machine (CFM), which is a convex variant of the widely used Factorization Machines (FMs).
no code implementations • NeurIPS 2014 • Kyle R. Ulrich, David E. Carlson, Wenzhao Lian, Jana S. Borg, Kafui Dzirasa, Lawrence Carin
The LFPs are modeled as a mixture of GPs, with state- and region-dependent mixture weights, and with the spectral content of the data encoded in GP spectral mixture covariance kernels.