Reinforcement learning holds the promise of enabling autonomous robots to learn large repertoires of behavioral skills with minimal human intervention. However, robotic applications of reinforcement learning often compromise the autonomy of the learning process in favor of achieving training times that are practical for real physical systems. This typically involves introducing hand-engineered ...

This paper discusses dexterous, within-hand manipulation with differential-type underactuated hands. We discuss the fact that not only can this class of hands, which to date have been considered almost exclusively for adaptive grasping, be utilized for precision manipulation, but also that the reduction of the number of actuators and constraints can make within-hand manipulation easier to imple...

Assessment of upper extremity motor skills often involves object manipulation, drawing or writing using a pencil, or performing specific gestures. Traditional assessment of such skills usually requires a trained person to record the time and accuracy resulting in a process that can be labor intensive and costly. Automating the entire assessment process will potentially lower the cost, produce e...

LEARNING AND GENERALIZING CONTROL-BASED GRASPING AND MANIPULATION SKILLS

This article proposes a method for learning and robotic replication of dynamic collaborative tasks from offline videos. The objective is to extend the concept demonstration (LfD) scenarios, benefiting widely available or easily producible To achieve this goal, we decode important information, such as Configuration Dependent Stiffness (CDS), which reveals contribution arm pose endpoint stiffness...

A cause-effect relation is called causality [1]. The causality relations with objects in an environment are known as affordances, defined as the acquired cause-effect relation of applying a behaviour/action on an entity/object to obtain an effect [2]. Affordance knowledge is part of the expertise needed by robots to properly perform tasks in an environment. This expertise is threefold: (i) know...