An Architectural Approach for Enabling and Developing Cooperative Behaviour in Diverse Autonomous Robots

The paper introduces an architecture for robot-to-robot cooperation which takes into consideration how situational context augmented with peer modeling fosters cooperation opportunity identification and cooperation planning. The presented architecture allows developing, training, testing, and deploying dynamic cooperation solutions for diverse autonomous robots using ontology-based reasoning. The architecture operates in three different worlds: in the Real World with real robots, in a 3D Virtual World by emulating the real environments and robots, and in an abstract Block World that enables developing and studying large-scale cooperation scenarios. We describe an assessment practice for our architecture and cooperation procedures, which is based on scenarios implemented in all three worlds, and provide initial results of stress testing the cooperation procedures in the Block World. Moreover, as the core part of our architecture can operate in all the three worlds, development of the robot cooperation with the architecture can regularly accommodate insights gained from experimenting and testing in one world as improvements in another. We report our insights from developing the architecture and cooperation procedures as additional research outcomes.Peer reviewe

Behavior adaptation for mobile robots via semantic map compositions of constraint-based controllers

Specifying and solving Constraint-based Optimization Problems (COP) has become a mainstream technology for advanced motion control of mobile robots. COP programming still requires expert knowledge to transform specific application context into the right configuration of the COP parameters (i.e., objective functions and constraints). The research contribution of this paper is a methodology to couple the context knowledge of application developers to the robot knowledge of control engineers, which, to our knowledge, has not yet been carried out. The former is offered a selected set of symbolic descriptions of the robots’ capabilities (its so-called “behavior semantics”) that are translated in control actions via “templates” in a “semantic map”; the latter contains the parameters that cover contextual dependencies in an application and robot vendor-independent way. The translation from semantics to control templates takes place in an “interaction layer” that contains 1) generic knowledge about robot motion capabilities (e.g., depending on the kinematic type of the robots), 2) spatial queries to extract relevant COP parameters from a semantic map (e.g., what is the impact of entering different types of “collision areas”), and 3) generic application knowledge (e.g., how the robots’ behavior is impacted by priorities, emergency, safety, and prudence). This particular design of, and interplay between, the application, interaction, and control layers provides a structured, conceptually simple approach to advance the complexity of mobile robot applications. Eventually, industry-wide cooperation between representatives of the application and control communities should result in an interaction layer with different standardized versions of semantic complexity.</p

Behavior adaptation for mobile robots via semantic map compositions of constraint-based controllers

Specifying and solving Constraint-based Optimization Problems (COP) has become a mainstream technology for advanced motion control of mobile robots. COP programming still requires expert knowledge to transform specific application context into the right configuration of the COP parameters (i.e., objective functions and constraints). The research contribution of this paper is a methodology to couple the context knowledge of application developers to the robot knowledge of control engineers, which, to our knowledge, has not yet been carried out. The former is offered a selected set of symbolic descriptions of the robots’ capabilities (its so-called “behavior semantics”) that are translated in control actions via “templates” in a “semantic map”; the latter contains the parameters that cover contextual dependencies in an application and robot vendor-independent way. The translation from semantics to control templates takes place in an “interaction layer” that contains 1) generic knowledge about robot motion capabilities (e.g., depending on the kinematic type of the robots), 2) spatial queries to extract relevant COP parameters from a semantic map (e.g., what is the impact of entering different types of “collision areas”), and 3) generic application knowledge (e.g., how the robots’ behavior is impacted by priorities, emergency, safety, and prudence). This particular design of, and interplay between, the application, interaction, and control layers provides a structured, conceptually simple approach to advance the complexity of mobile robot applications. Eventually, industry-wide cooperation between representatives of the application and control communities should result in an interaction layer with different standardized versions of semantic complexity

Semantic Systems. In the Era of Knowledge Graphs

This open access book constitutes the refereed proceedings of the 16th International Conference on Semantic Systems, SEMANTiCS 2020, held in Amsterdam, The Netherlands, in September 2020. The conference was held virtually due to the COVID-19 pandemic

Using Multi-Relational Embeddings as Knowledge Graph Representations for Robotics Applications

User demonstrations of robot tasks in everyday environments, such as households, can be brittle due in part to the dynamic, diverse, and complex properties of those environments. Humans can find solutions in ambiguous or unfamiliar situations by using a wealth of common-sense knowledge about their domains to make informed generalizations. For example, likely locations for food in a novel household. Prior work has shown that robots can benefit from reasoning about this type of semantic knowledge, which can be modeled as a knowledge graph of interrelated facts that define whether a relationship exists between two entities. Semantic reasoning about domain knowledge using knowledge graph representations has improved the robustness and usability of end user robots by enabling more fault tolerant task execution. Knowledge graph representations define the underlying representation of facts, how facts are organized, and implement semantic reasoning by defining the possible computations over facts (e.g. association, fact-prediction). This thesis examines the use of multi-relational embeddings as knowledge graph representations within the context of robust task execution and develops methods to explain the inferences of and sequentially train multi-relational embeddings. This thesis contributes: (i) a survey of knowledge graph representations that model semantic domain knowledge in robotics, (ii) the development and evaluation of our knowledge graph representation based on multi-relational embeddings, (iii) the integration of our knowledge graph representation into a robot architecture to improve robust task execution, (iv) the development and evaluation of methods to sequentially update multi-relational embeddings, and (v) the development and evaluation of an inference reconciliation framework for multi-relational embeddings.Ph.D

Maintaining Structured Experiences for Robots via Human Demonstrations: An Architecture To Convey Long-Term Robot\u2019s Beliefs

This PhD thesis presents an architecture for structuring experiences, learned through demonstrations, in a robot memory. To test our architecture, we consider a specific application where a robot learns how objects are spatially arranged in a tabletop scenario. We use this application as a mean to present a few software development guidelines for building architecture for similar scenarios, where a robot is able to interact with a user through a qualitative shared knowledge stored in its memory. In particular, the thesis proposes a novel technique for deploying ontologies in a robotic architecture based on semantic interfaces. To better support those interfaces, it also presents general-purpose tools especially designed for an iterative development process, which is suitable for Human-Robot Interaction scenarios. We considered ourselves at the beginning of the first iteration of the design process, and our objective was to build a flexible architecture through which evaluate different heuristic during further development iterations. Our architecture is based on a novel algorithm performing a oneshot structured learning based on logic formalism. We used a fuzzy ontology for dealing with uncertain environments, and we integrated the algorithm in the architecture based on a specific semantic interface. The algorithm is used for building experience graphs encoded in the robot\u2019s memory that can be used for recognising and associating situations after a knowledge bootstrapping phase. During this phase, a user is supposed to teach and supervise the beliefs of the robot through multimodal, not physical, interactions. We used the algorithm to implement a cognitive like memory involving the encoding, storing, retrieving, consolidating, and forgetting behaviours, and we showed that our flexible design pattern could be used for building architectures where contextualised memories are managed with different purposes, i.e. they contains representation of the same experience encoded with different semantics. The proposed architecture has the main purposes of generating and maintaining knowledge in memory, but it can be directly interfaced with perceiving and acting components if they provide, or require, symbolical knowledge. With the purposes of showing the type of data considered as inputs and outputs in our tests, this thesis also presents components to evaluate point clouds, engage dialogues, perform late data fusion and simulate the search of a target position. Nevertheless, our design pattern is not meant to be coupled only with those components, which indeed have a large room of improvement

Dynamics in Logistics

This open access book highlights the interdisciplinary aspects of logistics research. Featuring empirical, methodological, and practice-oriented articles, it addresses the modelling, planning, optimization and control of processes. Chiefly focusing on supply chains, logistics networks, production systems, and systems and facilities for material flows, the respective contributions combine research on classical supply chain management, digitalized business processes, production engineering, electrical engineering, computer science and mathematical optimization. To celebrate 25 years of interdisciplinary and collaborative research conducted at the Bremen Research Cluster for Dynamics in Logistics (LogDynamics), in this book hand-picked experts currently or formerly affiliated with the Cluster provide retrospectives, present cutting-edge research, and outline future research directions

Dynamics in Logistics

This open access book highlights the interdisciplinary aspects of logistics research. Featuring empirical, methodological, and practice-oriented articles, it addresses the modelling, planning, optimization and control of processes. Chiefly focusing on supply chains, logistics networks, production systems, and systems and facilities for material flows, the respective contributions combine research on classical supply chain management, digitalized business processes, production engineering, electrical engineering, computer science and mathematical optimization. To celebrate 25 years of interdisciplinary and collaborative research conducted at the Bremen Research Cluster for Dynamics in Logistics (LogDynamics), in this book hand-picked experts currently or formerly affiliated with the Cluster provide retrospectives, present cutting-edge research, and outline future research directions