Humanoids can learn motor skills through the programming by demonstration framework, which allows matching the kinematic movements of a robot with those of a human. Continuous goal-directed actions (CGDA) is a framework that can complement the paradigm of robot imitation. Instead of kinematic parameters, its encoding is centered on the changes an action produces on object features. The features can be any measurable characteristic of the object such as color, area, etc. The execution of actions encoded as CGDA allows a robot-configuration independent achievement of tasks, avoiding the correspondence problem. By tracking object features during action execution, we create a trajectory in an n-dimensional feature space that represents object temporal states, allowing generalization, recognition, and execution of action effects on the environment. Experiments have been performed, using a humanoid robot in a simulated environment. Evolutionary computation was used for joint parameter calculation of a humanoid robot. The objective is to generate a motor trajectory which leads to a feature trajectory equal to the objective one. In one of the experiments, the robot performs a spatial trajectory based on spatial object features. In a new experiment, the robot paints a wall by following a color feature encoding.
Robot learning frameworks, such as Programming by Demonstration, are based on learning tasks from sets of user demonstrations. These frameworks, in their naïve implementation, assume that all the data from the user demonstrations has been correctly sensed and can be relevant to the task. Analogous to feature selection, which is the process of selecting a subset of relevant features for use in model construction, this paper presents a demonstration selection process, which is additionally applied for feature selection for further data filtering.The demonstration and feature selection process presented is called Dissimilarity Mapping Filtering (DMF). DMF involves three steps: obtaining a measurement of dissimilarity (e.g. Dynamic Time Warping, etc.), reducing dimensions through a mapping algorithm (e.g. sum of dissimilarities, Multidimensional Scaling, etc.) and a filtering method (z-score based, DBSCAN, etc.). As a demonstration selector, DMF discards outlying demonstrations in terms of all the features considered simultaneously. As a feature selector, DMF discards features that present high inconsistency among demonstrations. We apply DMF to our Continuous Goal-Directed Actions (CGDA) robot learning framework presented in previous works.
We present Robot Devastation, a multiplayer augmented reality game using low-cost robots. Players can assemble their low-cost robotic platforms and connect them to the central server, commanding them through their home PCs. Several lowcost platforms were developed and tested inside the game.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.