Data-Driven Grasp Synthesis—A Survey

We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar, or unknown objects. This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique. In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects. Finally, for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps. Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping. We also draw a parallel to the classical approaches that rely on analytic formulations

Learning object, grasping and manipulation activities using hierarchical HMMs

This article presents a probabilistic algorithm for representing and learning complex manipulation activities performed by humans in everyday life. The work builds on the multi-level Hierarchical Hidden Markov Model (HHMM) framework which allows decomposition of longer-term complex manipulation activities into layers of abstraction whereby the building blocks can be represented by simpler action modules called action primitives. This way, human task knowledge can be synthesised in a compact, effective representation suitable, for instance, to be subsequently transferred to a robot for imitation. The main contribution is the use of a robust framework capable of dealing with the uncertainty or incomplete data inherent to these activities, and the ability to represent behaviours at multiple levels of abstraction for enhanced task generalisation. Activity data from 3D video sequencing of human manipulation of different objects handled in everyday life is used for evaluation. A comparison with a mixed generative-discriminative hybrid model HHMM/SVM (support vector machine) is also presented to add rigour in highlighting the benefit of the proposed approach against comparable state of the art techniques. © 2014 Springer Science+Business Media New York

Data for: Game Over or Play Again? Deploying games for promoting water recycling and hygienic practices at schools in Ethiopia

The data resulted from two small scale projects which took place at two schools in Ethiopia – in Adama and Sendafa, cities in the Oromia region. Constructed wetland for treating handwashing wastewater was constructed in a school in Adama, as part of a school WaSH improvement project (also new school latrines were constructed, and existing ones were renovated). The developed games “Clean and Green School” and “Water Go!” were designed around this intervention: latrines, handwashing facilities and constructed wetlands. The idea behind the games was developing educational instruments that would promote water recycling, handwashing activity and water reuse for toilet flushing and irrigation; to school students and school staff in an engaging way. By doing so, games can be played over and over again, so the students can be trained together with teachers and school staff involved in the operation and maintenance of the system (school guards and cleaning staff). Instead of delivering one time trainings, the idea was to incorporate innovative educational instruments (games) in school WaSH clubs curriculum. For the purpose of the second project – educational games around the F-diagram were developed and tested in a school in Sendafa (game WaSH quartet) and at both schools in Sendafa and Adama (Fly Over game). The sample sizes for the last testing session at locations, as reported in the manuscript are: Clean and Green School (n=8, Adama); Water Go! (n=6, Adama); WaSH Quartet (n=10, Sendafa); Fly Over (n=14, Sendafa and Adama). Though the number of students and school staff participating in evaluation was small, we could use it to observe dynamics, identify bottle necks and draw meaningful conclusions. However we do hope to scale the approach and conduct testing on more schools and children, obtaining statistically relevant results