1 code implementation • 9 Mar 2018 • Kuniyuki Takahashi, Jethro Tan
Estimation of tactile properties from vision, such as slipperiness or roughness, is important to effectively interact with the environment.
no code implementations • 17 Oct 2017 • Ayaka Kume, Eiichi Matsumoto, Kuniyuki Takahashi, Wilson Ko, Jethro Tan
To solve this problem, we propose Map-based Multi-Policy Reinforcement Learning (MMPRL), which aims to search and store multiple policies that encode different behavioral features while maximizing the expected reward in advance of the environment change.
1 code implementation • 17 Oct 2017 • Jun Hatori, Yuta Kikuchi, Sosuke Kobayashi, Kuniyuki Takahashi, Yuta Tsuboi, Yuya Unno, Wilson Ko, Jethro Tan
In this paper, we propose the first comprehensive system that can handle unconstrained spoken language and is able to effectively resolve ambiguity in spoken instructions.
no code implementations • 18 Oct 2016 • Carlos Hernandez, Mukunda Bharatheesha, Wilson Ko, Hans Gaiser, Jethro Tan, Kanter van Deurzen, Maarten de Vries, Bas Van Mil, Jeff van Egmond, Ruben Burger, Mihai Morariu, Jihong Ju, Xander Gerrmann, Ronald Ensing, Jan Van Frankenhuyzen, Martijn Wisse
This paper describes Team Delft's robot, which won the Amazon Picking Challenge 2016, including both the Picking and the Stowing competitions.