4,997 research outputs found
Neural Task Programming: Learning to Generalize Across Hierarchical Tasks
In this work, we propose a novel robot learning framework called Neural Task
Programming (NTP), which bridges the idea of few-shot learning from
demonstration and neural program induction. NTP takes as input a task
specification (e.g., video demonstration of a task) and recursively decomposes
it into finer sub-task specifications. These specifications are fed to a
hierarchical neural program, where bottom-level programs are callable
subroutines that interact with the environment. We validate our method in three
robot manipulation tasks. NTP achieves strong generalization across sequential
tasks that exhibit hierarchal and compositional structures. The experimental
results show that NTP learns to generalize well to- wards unseen tasks with
increasing lengths, variable topologies, and changing objectives.Comment: ICRA 201
MROS: Runtime Adaptation For Robot Control Architectures
Known attempts to build autonomous robots rely on complex control
architectures, often implemented with the Robot Operating System platform
(ROS). Runtime adaptation is needed in these systems, to cope with component
failures and with contingencies arising from dynamic environments-otherwise,
these affect the reliability and quality of the mission execution. Existing
proposals on how to build self-adaptive systems in robotics usually require a
major re-design of the control architecture and rely on complex tools
unfamiliar to the robotics community. Moreover, they are hard to reuse across
applications.
This paper presents MROS: a model-based framework for run-time adaptation of
robot control architectures based on ROS. MROS uses a combination of
domain-specific languages to model architectural variants and captures mission
quality concerns, and an ontology-based implementation of the MAPE-K and
meta-control visions for run-time adaptation. The experiment results obtained
applying MROS in two realistic ROS-based robotic demonstrators show the
benefits of our approach in terms of the quality of the mission execution, and
MROS' extensibility and re-usability across robotic applications
An Architectural Framework for Modeling Teleoperated service robots
Teleoperated robots are used to perform tasks that human operators cannot carry out because of the nature of the tasks themselves or the hostile nature of the working environment. Though many control architectures have been defined for developing these kinds of systems reusing common components, none has attained all its objectives because of the high variability of system behaviors.
This paper presents a new architectural approach that takes into account the latest advances in robotic architectures while adopting a component-oriented approach. This approach provides a common framework for developing robotized systems with very different behaviors and for integrating intelligent components. The architecture is currently being used, tested and improved in the development of a family of teleoperated robots which perform cleaning of ship-hull surfaces.Este trabajo está parcialmente financiado por CICYT ref. TIC2003-07804-C05-02 y la Comunidad Autónoma de Murcia (Séneca PB/5/FS/02
Designing Trustworthy Autonomous Systems
The design of autonomous systems is challenging and ensuring their trustworthiness can have different meanings, such as i) ensuring consistency and completeness of the requirements by a correct elicitation and formalization process; ii) ensuring that requirements are correctly mapped to system implementations so that any system behaviors never violate its requirements; iii) maximizing the reuse of available components and subsystems in order to cope with the design complexity; and iv) ensuring correct coordination of the system with its environment.Several techniques have been proposed over the years to cope with specific problems. However, a holistic design framework that, leveraging on existing tools and methodologies, practically helps the analysis and design of autonomous systems is still missing. This thesis explores the problem of building trustworthy autonomous systems from different angles. We have analyzed how current approaches of formal verification can provide assurances: 1) to the requirement corpora itself by formalizing requirements with assume/guarantee contracts to detect incompleteness and conflicts; 2) to the reward function used to then train the system so that the requirements do not get misinterpreted; 3) to the execution of the system by run-time monitoring and enforcing certain invariants; 4) to the coordination of the system with other external entities in a system of system scenario and 5) to system behaviors by automatically synthesize a policy which is correct
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