1 code implementation • 30 Sep 2021 • Yunke Ao, Le Chen, Florian Tschopp, Michel Breyer, Andrei Cramariuc, Roland Siegwart
Our approach models the calibration process compactly using model-free deep reinforcement learning to derive a policy that guides the motions of a robotic arm holding the sensor to efficiently collect measurements that can be used for both camera intrinsic calibration and camera-IMU extrinsic calibration.
1 code implementation • 4 Jan 2021 • Michel Breyer, Jen Jen Chung, Lionel Ott, Roland Siegwart, Juan Nieto
General robot grasping in clutter requires the ability to synthesize grasps that work for previously unseen objects and that are also robust to physical interactions, such as collisions with other objects in the scene.
Robotics
1 code implementation • 4 Nov 2020 • Le Chen, Yunke Ao, Florian Tschopp, Andrei Cramariuc, Michel Breyer, Jen Jen Chung, Roland Siegwart, Cesar Cadena
Visual-inertial systems rely on precise calibrations of both camera intrinsics and inter-sensor extrinsics, which typically require manually performing complex motions in front of a calibration target.
no code implementations • 2 Apr 2020 • Kenneth Blomqvist, Michel Breyer, Andrei Cramariuc, Julian Förster, Margarita Grinvald, Florian Tschopp, Jen Jen Chung, Lionel Ott, Juan Nieto, Roland Siegwart
With humankind facing new and increasingly large-scale challenges in the medical and domestic spheres, automation of the service sector carries a tremendous potential for improved efficiency, quality, and safety of operations.
1 code implementation • 13 Mar 2018 • Michel Breyer, Fadri Furrer, Tonci Novkovic, Roland Siegwart, Juan Nieto
We learn closed-loop policies mapping depth camera inputs to motion commands and compare different approaches to keep the problem tractable, including reward shaping, curriculum learning and using a policy pre-trained on a task with a reduced action set to warm-start the full problem.
Robotics