Learning Stepping Motions for Fall Avoidance with Reinforcement Learning
نویسندگان
چکیده
منابع مشابه
Obstacle Avoidance through Reinforcement Learning
A method is described for generating plan-like, reflexive, obstacle avoidance behaviour in a mobile robot. The experiments reported here use a simulated vehicle with a primitive range sensor. Avoidance behaviour is encoded as a set of continuous functions of the perceptual input space. These functions are stored using CMACs and trained by a variant of Barto and Sutton's adaptive critic algorith...
متن کاملUncertainty-Aware Reinforcement Learning for Collision Avoidance
Reinforcement learning can enable complex, adaptive behavior to be learned automatically for autonomous robotic platforms. However, practical deployment of reinforcement learning methods must contend with the fact that the training process itself can be unsafe for the robot. In this paper, we consider the specific case of a mobile robot learning to navigate an a priori unknown environment while...
متن کاملReinforcement Learning with Reusing Mechanism of Avoidance Actions and its Application to Learning Whole-Body Motions of Multi-Link Robot∗
In acquiring a motion only from its objective by learning, large cost such as damage from falling over and a large number of trials are required if the motion is a complex one, such as a jumping serve. Reusing the knowledge already learnt is an essential mechanism to learn such motions efficiently, like humans do. In this study, we propose to use a decomposition of action-value functions as a r...
متن کاملA fuzzy controller with supervised learning assisted reinforcement learning algorithm for obstacle avoidance
Fuzzy logic systems are promising for efficient obstacle avoidance. However, it is difficult to maintain the correctness, consistency, and completeness of a fuzzy rule base constructed and tuned by a human expert. A reinforcement learning method is capable of learning the fuzzy rules automatically. However, it incurs a heavy learning phase and may result in an insufficiently learned rule base d...
متن کاملDynamic Obstacle Avoidance with PEARL: PrEference Appraisal Reinforcement Learning
Manual derivation of optimal robot motions for task completion is difficult, especially when a robot is required to balance its actions between opposing preferences. One solution has been to automatically learn near optimal motions with Reinforcement Learning (RL). This has been successful for several tasks including swing-free UAV flight, table tennis, and autonomous driving. However, high-dim...
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: Journal of the Robotics Society of Japan
سال: 2009
ISSN: 0289-1824,1884-7145
DOI: 10.7210/jrsj.27.527