Feedback-based Fabric Strip Folding

Accurate manipulation of a deformable body such as a piece of fabric is difficult because of its many degrees of freedom and unobservable properties affecting its dynamics. To alleviate these challenges, we propose the application of feedback-based control to robotic fabric strip folding. The feedback is computed from the low dimensional state extracted from a camera image. We trained the controller using reinforcement learning in simulation which was calibrated to cover the real fabric strip behaviors. The proposed feedback-based folding was experimentally compared to two state-of-the-art folding methods and our method outperformed both of them in terms of accuracy.Comment: Submitted to IEEE/RSJ IROS201

Learning to Manipulate Tools by Aligning Simulation to Video Demonstration

International audienceA seamless integration of robots into human environments requires robots to learn how to use existing human tools. Current approaches for learning tool manipulation skills mostly rely on expert demonstrations provided in the target robot environment, for example, by manually guiding the robot manipulator or by teleoperation. In this work, we introduce an automated approach that replaces an expert demonstration with a Youtube video for learning a tool manipulation strategy. The main contributions are twofold. First, we design an alignment procedure that aligns the simulated environment with the realworld scene observed in the video. This is formulated as an optimization problem that finds a spatial alignment of the tool trajectory to maximize the sparse goal reward given by the environment. Second, we devise an imitation learning approach that focuses on the trajectory of the tool and how it interacts with the environment, rather than the motion of the human. We demonstrate the proposed approach on spade, scythe and hammer tools in simulation, and show the effectiveness of the trained policy for the spade on a real Franka Emika Panda robot demonstration

Learning to Manipulate Tools by Aligning Simulation to Video Demonstration

International audienceA seamless integration of robots into human environments requires robots to learn how to use existing human tools. Current approaches for learning tool manipulation skills mostly rely on expert demonstrations provided in the target robot environment, for example, by manually guiding the robot manipulator or by teleoperation. In this work, we introduce an automated approach that replaces an expert demonstration with a Youtube video for learning a tool manipulation strategy. The main contributions are twofold. First, we design an alignment procedure that aligns the simulated environment with the realworld scene observed in the video. This is formulated as an optimization problem that finds a spatial alignment of the tool trajectory to maximize the sparse goal reward given by the environment. Second, we devise an imitation learning approach that focuses on the trajectory of the tool and how it interacts with the environment, rather than the motion of the human. We demonstrate the proposed approach on spade, scythe and hammer tools in simulation, and show the effectiveness of the trained policy for the spade on a real Franka Emika Panda robot demonstration

Learning Object Manipulation Skills via Approximate State Estimation from Real Videos

CoRL 2020, code at https://github.com/makarandtapaswi/Real2Sim_CoRL2020, project page at https://data.ciirc.cvut.cz/public/projects/2020Real2Sim/International audienceHumans are adept at learning new tasks by watching a few instructional videos. On the other hand, robots that learn new actions either require a lot of effort through trial and error, or use expert demonstrations that are challenging to obtain. In this paper, we explore a method that facilitates learning object manipulation skills directly from videos. Leveraging recent advances in 2D visual recognition and differentiable rendering, we develop an optimization based method to estimate a coarse 3D state representation for the hand and the manipulated object(s) without requiring any supervision. We use these trajectories as dense rewards for an agent that learns to mimic them through reinforcement learning. We evaluate our method on simple single-and two-object actions from the Something-Something dataset. Our approach allows an agent to learn actions from single videos, while watching multiple demonstrations makes the policy more robust. We show that policies learned in a simulated environment can be easily transferred to a real robot

Analysis of supply of ERP systems for small and medium businesses delivered by SaaS in the Czech Republic

The aim of this work is the issue of Enterprise Resource Planning offered by way of Systems as a service and the suitability of this implementation for small and medium enterprises. Theoretical part describes the genesis and development of Enterprise Resource Planning, their functionality and purpose. There's also specified the term Cloud computing, its principles, advantages and disadvantages and options of distribution. One of the principles of the distribution, System as a Service is described in detail, including the necessary Service-level agreement, and its capabilities in providing Enterprise Resource Planning. The practical part of the work consists of developing a comprehensive overview of the Czech market of Enterprise Resource Planning offered in the form of rent System as a Service. This list is analyzed from the perspective of a key customer for this product, namely Small and medium enterprises. The last part of this work consists of obtaining customer references and supplier experiences from already realized projects