![]() Keywords: Human-Robot Collaboration, Intention Recognition, Industrial RobotsĪbstract: Human action recognition and motion forecasting is becoming increasingly successful, in particular with utilizing graphs. ![]() Real time simulation results show that the proposed framework is capable of predicting the user intentions after change point detection.Įxploiting Spatio-Temporal Human-Object Relations Using Graph Neural Networks for Human Action Recognition and 3D Motion Forecasting Also, it deals with variable motion sizes. Moreover, the proposed framework presents just-in-time prediction of the 3D motions by proposing a change point detection technique, which allows the framework to autonomously identify when to conduct a prediction. However, since most UAVs tasks are in a 3D world, this paper introduces 3D Autocomplete for 3D motions. Previous Autocomplete systems focused on different 2D motions (line, arc, sine.). This has been shown to improve the performance of the system and reduce operator workload. ![]() Such a framework uses machine learning to recognize and classify human inputs as one of a set of motion primitives, and then, if the human operator accepts, synthesizes the motion in order to complete the desired motion. Autocomplete addresses this problem by automatically identifying and completing the user's intended motion. Keywords: Human-Robot Collaboration, Deep Learning MethodsĪbstract: Tele-operating aerial vehicles without any automated assistance is challenging due to various limitations, especially for inexperienced users. Further details and video results can be found at https: ///.Īutocomplete of 3D Motions for UAV Teleoperation In addition, SEED also exhibits a substantial reduction of human effort compared to other RLHF methods. Our results show that SEED significantly outperforms state-of-the-art RL algorithms in sample efficiency and safety. To evaluate the performance of SEED, we conducted extensive experiments on five manipulation tasks with varying levels of complexity. This feature makes the training process even safer and more efficient. Additionally, parameterized skills provide a clear view of the agent’s high-level intentions, allowing humans to evaluate skill choices before they are executed. By combining them, SEED reduces the human effort required in RLHF and increases safety in training robot manipulation with RL in real-world settings. Both approaches are particularly effective in addressing sparse re- ward issues and the complexities involved in long-horizon tasks. To overcome these challenges, we propose a novel framework, SEED, which leverages two approaches: reinforcement learning from human feedback (RLHF) and primitive skill-based reinforcement learning. Keywords: Human Factors and Human-in-the-Loop, Reinforcement Learning, Machine Learning for Robot ControlĪbstract: Reinforcement learning (RL) algorithms face sig- nificant challenges when dealing with long-horizon robot ma- nipulation tasks in real-world environments due to sample inefficiency and safety issues.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |