Advances in Human-Robot Interaction

Rapid advances in the field of robotics have made it possible to use robots not just in industrial automation but also in entertainment, rehabilitation, and home service. Since robots will likely affect many aspects of human existence, fundamental questions of human-robot interaction must be formulated and, if at all possible, resolved. Some of these questions are addressed in this collection of papers by leading HRI researchers

Advancing automation and robotics technology for the space station and for the US economy: Submitted to the United States Congress October 1, 1987

In April 1985, as required by Public Law 98-371, the NASA Advanced Technology Advisory Committee (ATAC) reported to Congress the results of its studies on advanced automation and robotics technology for use on the space station. This material was documented in the initial report (NASA Technical Memorandum 87566). A further requirement of the Law was that ATAC follow NASA's progress in this area and report to Congress semiannually. This report is the fifth in a series of progress updates and covers the period between 16 May 1987 and 30 September 1987. NASA has accepted the basic recommendations of ATAC for its space station efforts. ATAC and NASA agree that the mandate of Congress is that an advanced automation and robotics technology be built to support an evolutionary space station program and serve as a highly visible stimulator affecting the long-term U.S. economy

On the Integration of Adaptive and Interactive Robotic Smart Spaces

© 2015 Mauro Dragone et al.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. (CC BY-NC-ND 3.0)Enabling robots to seamlessly operate as part of smart spaces is an important and extended challenge for robotics R&D and a key enabler for a range of advanced robotic applications, such as AmbientAssisted Living (AAL) and home automation. The integration of these technologies is currently being pursued from two largely distinct view-points: On the one hand, people-centred initiatives focus on improving the user’s acceptance by tackling human-robot interaction (HRI) issues, often adopting a social robotic approach, and by giving to the designer and - in a limited degree – to the final user(s), control on personalization and product customisation features. On the other hand, technologically-driven initiatives are building impersonal but intelligent systems that are able to pro-actively and autonomously adapt their operations to fit changing requirements and evolving users’ needs,but which largely ignore and do not leverage human-robot interaction and may thus lead to poor user experience and user acceptance. In order to inform the development of a new generation of smart robotic spaces, this paper analyses and compares different research strands with a view to proposing possible integrated solutions with both advanced HRI and online adaptation capabilities.Peer reviewe

Rask Policy-Læring Gjennom Imitation Learning og Reinforcement Learning i Løfteoperasjoner

I denne forskningen ble implementering og evaluering av en ny læringsmetode for autonom kranoperasjon kalt LOKI-G (Locally Optimal search after K-step Imitation - Generalized) ved hjelp av Closed-form Continous-time (CfC) Artificial Neural Network (ANN) utforsket. Studien dreide seg om å takle Sim-to-real gapet ved å tillate modellen å lære "on edge" med minimale eksempler, noe som reduserer behovet for simulatorer. Det ble lagt vekt på å skape en effektiv, robust, pålitelig og forklarlig modell som kunne trenes for anvendelser i den virkelige verden. Forskningen involverte fem eksperimenter hvor modellens ytelse under varierende forhold ble gransket. Modellens reaksjon under basisforhold, sensorisk deprivasjon, endret målposisjon og objektgeneralisering ga betydelige innsikter i modellens evner og potensielle områder for forbedring. Resultatene demonstrerte CfC ANN's evne til å lære den grunnleggende oppgaven med høy nøyaktighet, og viste pålitelig oppførsel og utmerket ytelse under Zero-Shot Learning. Modellen viste imidlertid begrensninger med hensyn til å forstå dybde. Disse funnene har betydelige konsekvenser for å akselerere utviklingen av autonomi i kraner, noe som øker industriell effektivitet og sikkerhet, reduserer karbonutslipp og baner vei for bred adopsjon av autonome løfteoperasjoner. Fremtidige forskningsretninger antyder potensialet for å forbedre modellen ved å optimalisere hyperparametre, utvide modellen til multimodal operasjon, forbedre sikkerhet gjennom bruk av BarrierNet, og adoptere nye læringsmetoder for raskere konvergens. Refleksjoner om viktigheten av å vente under oppgaver og mengden og kvaliteten på data for trening dukket også opp i studien. Som konklusjon har dette arbeidet gitt et eksperimentelt bevis på konsept og et springbrett for fremtidig forskning i utviklingen av tilpasningsdyktige, robuste og pålitelige AI-modeller for autonome industrioperasjoner.In this research, the implementation and evaluation of a novel learning approach for an autonomous crane operation called LOKI-G (Locally Optimal search after K-step Imitation - Generalized) using Closed-form Continous-time (CfC) Artificial Neural Network (ANN) was explored. The study revolved around addressing the Sim-to-real gap by allowing the model to learn on edge with minimal examples, mitigating the need for simulators. An emphasis was placed on creating a sparse, robust, reliable, and explainable model that could be trained for real-world applications. The research involved five experiments where the model's performance under varying conditions was scrutinized. The model's response under baseline conditions, sensory deprivation, altered environment, and object generalization provided significant insights into the model's capabilities and potential areas for improvement. The results demonstrated the CfC ANN's ability to learn the fundamental task with high accuracy, exhibiting reliable behaviour and excellent performance during Zero-Shot Learning. The model, however, showed limitations in regard to understanding depth. These findings have significant implications for accelerating the development of autonomy in cranes, thus increasing industrial efficiency and safety, reducing carbon emissions and paving the way for the wide-scale adoption of autonomous lifting operations. Future research directions suggest the potential of improving the model by optimizing hyperparameters, extending the model to multimodal operation, ensuring safety through the application of BarrierNet, and adopting new learning methods for faster convergence. Reflections on the importance of waiting during tasks and the quantity and quality of data for training also surfaced during the study. In conclusion, this work has provided an experimental proof of concept and a springboard for future research into the development of adaptable, robust, and trustworthy AI models for autonomous industrial operations

Reducing the Barrier to Entry of Complex Robotic Software: a MoveIt! Case Study

Developing robot agnostic software frameworks involves synthesizing the disparate fields of robotic theory and software engineering while simultaneously accounting for a large variability in hardware designs and control paradigms. As the capabilities of robotic software frameworks increase, the setup difficulty and learning curve for new users also increase. If the entry barriers for configuring and using the software on robots is too high, even the most powerful of frameworks are useless. A growing need exists in robotic software engineering to aid users in getting started with, and customizing, the software framework as necessary for particular robotic applications. In this paper a case study is presented for the best practices found for lowering the barrier of entry in the MoveIt! framework, an open-source tool for mobile manipulation in ROS, that allows users to 1) quickly get basic motion planning functionality with minimal initial setup, 2) automate its configuration and optimization, and 3) easily customize its components. A graphical interface that assists the user in configuring MoveIt! is the cornerstone of our approach, coupled with the use of an existing standardized robot model for input, automatically generated robot-specific configuration files, and a plugin-based architecture for extensibility. These best practices are summarized into a set of barrier to entry design principles applicable to other robotic software. The approaches for lowering the entry barrier are evaluated by usage statistics, a user survey, and compared against our design objectives for their effectiveness to users