FailRecOnt - An ontology-based framework for failure interpretation and recovery in planning and execution

Autonomous mobile robot manipulators have the potential to act as robot helpers at home to improve quality of life for various user populations, such as elderly or handicapped people, or to act as robot co-workers on factory floors, helping in assembly applications where collaborating with other operators may be required. However, robotic systems do not show robust performance when placed in environments that are not tightly controlled. An important cause of this is that failure handling often consists of scripted responses to foreseen complications, which leaves the robot vulnerable to new situations and ill-equipped to reason about failure and recovery strategies. Instead of libraries of hard-coded reactions that are expensive to develop and maintain, more sophisticated reasoning mechanisms are needed to handle failure. This requires an ontological characterization of what failure is, what concepts are useful to formulate causal explanations of failure, and integration with knowledge of available resources including the capabilities of the robot as well as those of other potential cooperative agents in the environment, e.g. a human user. We propose the FailRecOnt framework as a step in this direction. We have integrated an ontology for failure interpretation and recovery with a contingency-based task and motion planning framework such that a robot can deal with uncertainty, recover from failures, and deal with human-robot interactions. A motivating example has been introduced to justify this proposal. The proposal has been tested with a challenging scenarioPeer ReviewedPostprint (published version

“Knowing from” – An outlook on ontology enabled knowledge transfer for robotic systems

Encoding practical knowledge about everyday activities has proven difficult, and is a limiting factor in the progress of autonomous robotics. Learning approaches, e.g. imitation learning from human data, have been used as a way to circumvent this difficulty. While such approaches are on the right track, they require comprehensive knowledge modelling about the data present in records of activity episodes, and about the skills one attempts to have the robot learn. We provide a list of competency questions such knowledge modelling should answer, summarize some recent developments in this direction, and finish with a few open problems.Postprint (author's final draft

An ontology for failure interpretation in automated planning and execution

This is a post-peer-review, pre-copyedit version of an article published in ROBOT - Iberian Robotics Conference. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-35990-4_31”.Autonomous indoor robots are supposed to accomplish tasks, like serve a cup, which involve manipulation actions, where task and motion planning levels are coupled. In both planning levels and execution phase, several source of failures can occur. In this paper, an interpretation ontology covering several sources of failures in automated planning and also during the execution phases is introduced with the purpose of working the planning more informed and the execution prepared for recovery. The proposed failure interpretation ontological module covers: (1) geometric failures, that may appear when e.g. the robot can not reach to grasp/place an object, there is no free-collision path or there is no feasible Inverse Kinematic (IK) solution. (2) hardware related failures that may appear when e.g. the robot in a real environment requires to be re-calibrated (gripper or arm), or it is sent to a non-reachable configuration. (3) software agent related failures, that may appear when e.g. the robot has software components that fail like when an algorithm is not able to extract the proper features. The paper describes the concepts and the implementation of failure interpretation ontology in several foundations like DUL and SUMO, and presents an example showing different situations in planning demonstrating the range of information the framework can provide for autonomous robotsPeer Reviewe

Tomorrow's Europe : the views of those concerned

845 p., ref. bib. : 37 p