Fetch & Freight : Standard Platforms for Service Robot Applications

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CASSL: Curriculum Accelerated Self-Supervised Learning (2017, methodology): “The grasping experiments and data are collected on a Fetch mobile manipulator [31].”
Lyapunov-based Safe Policy Optimization for Continuous Control (2019, methodology): “Finally, we deployed the SL-DDPG policy onto the real Fetch robot (Melonee Wise & Dymesich, 2016) in an every-day ofﬁce environment.”
Learning Navigation Behaviors End-to-End With AutoRL (2018, methodology): “First, we investigate the difference between simulation and reality when PF and P2P policies were deployed on the Fetch robot [27].”
Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos (2018, methodology): “Finally, we verify the approach in an indoor environment setting, by testing on data collected by the Fetch robot (Wise et al. 2016).”
Don’t Forget to Buy Milk: Contextually Aware Grocery Reminder Household Robot (2022, methodology): “We integrate our architecture on the Fetch mobile manipulator robot [19], and test it in a large indoor space with four different contexts, and 10 different household objects.”
Time-Contrastive Networks: Self-Supervised Learning from Multi-view Observation (2017, methodology): “We use a Fetch robot[30] in our experiment, which requires a fair amount of training and concentration for humans to imitate.”