Contextual policy search for linear and nonlinear generalization of a humanoid walking controller

We investigate learning of flexible robot locomotion controllers, i.e., the controllers should be applicable for multiple contexts, for example different walking speeds, various slopes of the terrain or other physical properties of the robot. In our experiments, contexts are desired walking linear speed of the gait. Current approaches for learning control parameters of biped locomotion controllers are typically only applicable for a single context. They can be used for a particular context, for example to learn a gait with highest speed, lowest energy consumption or a combination of both. The question of our research is, how can we obtain a flexible walking controller that controls the robot (near) optimally for many different contexts? We achieve the desired flexibility of the controller by applying the recently developed contextual relative entropy policy search(REPS) method which generalizes the robot walking controller for different contexts, where a context is described by a real valued vector. In this paper we also extend the contextual REPS algorithm to learn a non-linear policy instead of a linear policy over the contexts which call it RBF-REPS as it uses Radial Basis Functions. In order to validate our method, we perform three simulation experiments including a walking experiment using a simulated NAO humanoid robot. The robot learns a policy to choose the controller parameters for a continuous set of forward walking speeds

Socially assistive robots : the specific case of the NAO

Numerous researches have studied the development of robotics, especially socially assistive robots (SAR), including the NAO robot. This small humanoid robot has a great potential in social assistance. The NAO robot’s features and capabilities, such as motricity, functionality, and affective capacities, have been studied in various contexts. The principal aim of this study is to gather every research that has been done using this robot to see how the NAO can be used and what could be its potential as a SAR. Articles using the NAO in any situation were found searching PSYCHINFO, Computer and Applied Sciences Complete and ACM Digital Library databases. The main inclusion criterion was that studies had to use the NAO robot. Studies comparing it with other robots or intervention programs were also included. Articles about technical improvements were excluded since they did not involve concrete utilisation of the NAO. Also, duplicates and articles with an important lack of information on sample were excluded. A total of 51 publications (1895 participants) were included in the review. Six categories were defined: social interactions, affectivity, intervention, assisted teaching, mild cognitive impairment/dementia, and autism/intellectual disability. A great majority of the findings are positive concerning the NAO robot. Its multimodality makes it a SAR with potential