Reinforcement Learning Based User-Guided Motion Planning for Human-Robot Collaboration
نویسندگان
چکیده
منابع مشابه
Robot motion adaptation through user intervention and reinforcement learning∗
Assistant robots are designed to perform specific tasks for the user, but their performance is rarely optimal, hence they are required to adapt to user preferences or new task requirements. In the previous work, the potential of an interactive learning framework based on user intervention and reinforcement learning (RL) was assessed. The framework allowed the user to correct an unfitted segment...
متن کاملVisual Techniques for User-Guided Motion Planning
Robotic motion planning is an ubiquitous field of study, with innumerable applications in science, engineering, and beyond. A majority of research has focused on fully automated strategies, which have led to great progress in confronting a variety of interesting scenarios. At its core, however, motion planning is simply infeasible for most complex problems. Automatic planners lack insight and c...
متن کاملSoftware for project-based learning of robot motion planning
Motion planning is a core problem in robotics concerned with finding feasible paths for a given robot. Motion planning algorithms perform a search in the high-dimensional continuous space of robot configurations and exemplify many of the core algorithmic concepts of search algorithms and associated data structures. Motion planning algorithms can be explained in a simplified two-dimensional sett...
متن کاملLearning Sampling Distributions for Robot Motion Planning
A defining feature of sampling-based motion planning is the reliance on an implicit representation of the state space, which is enabled by a set of probing samples. Traditionally, these samples are drawn either probabilistically or deterministically to uniformly cover the state space. Yet, the motion of many robotic systems is often restricted to “small” regions of the state space, due to e.g. ...
متن کاملBiological Robot Arm Motion through Reinforcement Learning
The present paper discusses an optimal control method of biological robot arm which has redundancy of the mapping from the control input to the task goal. The control input space is divided into a couple of subspaces according to a priority order depending on the progress and stability of learning. In the proposed method, the search noise which is required for reinforcement learning is restrict...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Social Science Research Network
سال: 2022
ISSN: ['1556-5068']
DOI: https://doi.org/10.2139/ssrn.4045878