Michel Breyer

ETH Zurich — Switzerland

Engineering · Physical Sciences

7h-index250citations16works0.02yr avg
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Research Topics

Robot Manipulation and Learning(11), Robotics and Sensor-Based Localization(8), Reinforcement Learning in Robotics(6), Robotic Path Planning Algorithms(5), Soft Robotics and Applications(5)

Publications16 total

Closed-Loop Next-Best-View Planning for Target-Driven Grasping
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)·2022· 20 cited
Closed-Loop Next-Best-View Planning for Target-Driven Grasping
arXiv (Cornell University)·2022· 1 citedOpen Access
Unified Data Collection for Visual-Inertial Calibration via Deep Reinforcement Learning
2022 International Conference on Robotics and Automation (ICRA)·2022· 3 cited
Unified Data Collection for Visual-Inertial Calibration via Deep Reinforcement Learning
arXiv (Cornell University)·2021Open Access
Efficient Multi-scale POMDPs for Robotic Object Search and Delivery
2021· 7 citedOpen Access
Volumetric Grasping Network: Real-time 6 DOF Grasp Detection in Clutter
arXiv (Cornell University)·2021· 44 citedOpen Access
Learning Trajectories for Visual-Inertial System Calibration via Model-based Heuristic Deep Reinforcement Learning
Repository for Publications and Research Data (ETH Zurich)·2021· 1 citedOpen Access
Learning Trajectories for Visual-Inertial System Calibration via Model-based Heuristic Deep Reinforcement Learning
arXiv (Cornell University)·2020· 2 citedOpen Access
Object Finding in Cluttered Scenes Using Interactive Perception
2020· 64 cited
Go Fetch: Mobile Manipulation in Unstructured Environments
arXiv (Cornell University)·2020· 12 citedOpen Access
Volumetric Grasping Network: Real-time 6 DOF Grasp Detection in Clutter
Repository for Publications and Research Data (ETH Zurich)·2020· 6 citedOpen Access
Object Finding in Cluttered Scenes Using Interactive Perception
arXiv (Cornell University)·2019· 6 citedOpen Access
Dex-Net MM: Deep Grasping for Surface Decluttering with a Low-Precision Mobile Manipulator
2019· 10 cited
Comparing Task Simplifications to Learn Closed-Loop Object Picking Using Deep Reinforcement Learning
IEEE Robotics and Automation Letters·2019· 53 citedOpen Access
Flexible Robotic Grasping with Sim-to-Real Transfer based Reinforcement Learning.
2018· 20 cited
Comparing Task Simplifications to Learn Closed-Loop Object Picking Using Deep Reinforcement Learning
arXiv (Cornell University)·2018· 1 citedOpen Access

Frequent Co-authors

Roland Siegwart(16), Juan Nieto(9), Jen Jen Chung(8), Tonči Novković(5), Fadri Furrer(5), Lionel Ott(5), Andrei Cramariuc(5), Florian Tschopp(5), Yunke Ao(4), Rémi Pautrat(2), Julian Förster(2), Le Chen(2), César Cadena(2), Le Chen(2), Kenneth Blomqvist(1), Margarita Grinvald(1), Benno Staub(1), Ajay Kumar Tanwani(1), Jeffrey Mahler(1), Michael Laskey(1)