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Found 3 papers, 1 papers with code
Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration
We propose a technique for multi-task learning from demonstration that trains the controller of a low-cost robotic arm to accomplish several complex picking and placing tasks, as well as non-prehensile manipulation.
Internet of Things Applications: Animal Monitoring with Unmanned Aerial Vehicle
To achieve the information efficiently, we propose a path planning approach for the UAV based on a Markov decision process (MDP) model.
From virtual demonstration to real-world manipulation using LSTM and MDN
Our experimental studies validate the three contributions of the paper: (1) the controller learned from virtual demonstrations can be used to successfully perform the manipulation tasks on a physical robot, (2) the LSTM+MDN architectural choice outperforms other choices, such as the use of feedforward networks and mean-squared error based training signals and (3) allowing imperfect demonstrations in the training set also allows the controller to learn how to correct its manipulation mistakes.
