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Lower exoskeleton has gained considerable interests in rehabilitation and health care applications, since it has the potential to help the paraplegia patients to walk again. On the control of these lower exoskeletons, the predefined gait control methods are commonly used, which requires the pilot to adjust his/her movements to follow the predefined motion trajectories. In the meanwhile, the desired...
Human-powered lower exoskeletons have gained considerable interests from both academia and industry over the past few decades, and thus have seen increasing applications in areas of human locomotion assistance and strength augmentation. One of the most important aspects in those applications is to achieve robust control of lower exoskeletons, which, in the first place, requires the proactive modeling...
Learning by demonstration methods have gained considerable interest in human-coupled robot control. It aims at modeling the goal motion trajectories through human demonstration. However, in lower exoskeleton control, the physical human-robot interaction is changing from pilot to pilot or even for one pilot in different walking patterns. This characteristic requires that the exoskeletons should have...
Sensitivity Amplification Control (SAC) algorithm was first proposed in the augmentation applications of Berkeley Lower Extremity Exoskeleton (BLEEX). The SAC algorithm is widely used in human augmentation applications since it just need the information from the exoskeleton robot, so that the complexity of exoskeleton system can be reduced greatly. However, the SAC algorithm has two main drawbacks:...
Option has proven useful in discovering hierarchical structure in reinforcement learning to fasten learning. The key problem of automatic option discovery is to find subgoals. Though approaches based on visiting-frequency have gained much research focuses, many of them fail to distinguish subgoals from their nearby states. Based on the action-restricted property of subgoals we find, subgoals can be...
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