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20202023
most citedDROID: Minimizing the Reality Gap using Single-Shot Human Demonstration

1 citations · 2 across the 4 of their papers we have counts for

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6 papers · 1 filter

cs.RO2023

Crossing the Reality Gap in Tactile-Based Learning

Ya-Yen Tsai, Bidan Huang, Yu Zheng +3

Tactile sensors are believed to be essential in robotic manipulation, and prior works often rely on experts to reason the sensor feedback and design a controller. With the recent a…

cs.RO2023★ 1 cited

TacGNN:Learning Tactile-based In-hand Manipulation with a Blind Robot

Linhan Yang, Bidan Huang, Qingbiao Li +4

In this paper, we propose a novel framework for tactile-based dexterous manipulation learning with a blind anthropomorphic robotic hand, i.e. without visual sensing. First, object-…

cs.RO2021

Dual-arm Coordinated Manipulation for Object Twisting with Human Intelligence

Weibang Bai, Ningshan Zhang, Baoru Huang +5

Robotic dual-arm twisting is a common but very challenging task in both industrial production and daily services, as it often requires dexterous collaboration, a large scale of end…

cs.RO2021

Sim-to-Real Transfer for Robotic Manipulation with Tactile Sensory

Zihan Ding, Ya-Yen Tsai, Wang Wei Lee +1

Reinforcement Learning (RL) methods have been widely applied for robotic manipulations via sim-to-real transfer, typically with proprioceptive and visual information. However, the…

cs.RO2021★ 1 cited

DROID: Minimizing the Reality Gap using Single-Shot Human Demonstration

Ya-Yen Tsai, Hui Xu, Zihan Ding +3

Reinforcement learning (RL) has demonstrated great success in the past several years. However, most of the scenarios focus on simulated environments. One of the main challenges of…

cs.RO2020

Constrained-Space Optimization and Reinforcement Learning for Complex Tasks

Ya-Yen Tsai, Bo Xiao, Edward Johns +1

Learning from Demonstration is increasingly used for transferring operator manipulation skills to robots. In practice, it is important to cater for limited data and imperfect human…