text: We present a novel, reusable and task-agnostic primitive for assessing the outcome of a force-interaction robotic skill, useful e.g. for applications such as quality control in industrial manufacturing. The proposed method is easily programmed by kinesthetic teaching, and the desired adaptability and reusability are achieved by machine learning models. The primitive records sensory data during both demonstrations and reproductions of a movement. Recordings include the end-effector's Cartesian pose and exerted wrench at each time step. The collected data are then used to train Gaussian Processes which create models of the wrench as a function of the robot's pose. The similarity between the wrench models of the demonstration and the movement's reproduction is derived by measuring their Hellinger distance. This comparison creates features that are fed as inputs to a Naive Bayes classifier which estimates the movement's probability of success. The evaluation is performed on two diverse robotic assembly tasks -- snap-fitting and screwing -- with a total of 5 use cases, 11 demonstrations, and more than 200 movement executions. The performance metrics prove the proposed method's capability of generalization to different demonstrations and movements.
  @cite_3: Automating snap assemblies is highly desirable but challenging due to their varied geometrical configurations and elastic components. A key aspect to automating snap assemblies is robot state estimation and corrective motion generation, here defined as snap sensing. While progress is being made, there are yet no robust systems that allow for snap sensing. To this end we have integrated a framework that consists of a control strategy and control framework that generalises to cantilever snaps of varying geometrical complexity. We have also integrated a robot state verification method (RCBHT) that encodes FT data to yield high-level intuitive behaviours and perform output verification. Optimisation procedures and Bayesian filtering have been included in the RCBHT to increase robustness and granularity. The system provides belief states for higher level behaviours allowing probabilistic state estimation and outcome verification. In this work, preliminary assembly failure characterisation has been conducted an...
summary: Rojas al @cite_3 identify key segments of the wrench signals and create a task-specific hierarchical taxonomy based on its time derivative. The task outcome is indicated by the states at the highest level of this taxonomy. However, the segmentation threshold is predefined, and the taxonomy associated with segments is manually created which significantly reduces its applicability to different tasks.