31,281 research outputs found
Robot Task Planning and Situation Handling in Open Worlds
Automated task planning algorithms have been developed to help robots
complete complex tasks that require multiple actions. Most of those algorithms
have been developed for "closed worlds" assuming complete world knowledge is
provided. However, the real world is generally open, and the robots frequently
encounter unforeseen situations that can potentially break the planner's
completeness. This paper introduces a novel algorithm (COWP) for open-world
task planning and situation handling that dynamically augments the robot's
action knowledge with task-oriented common sense. In particular, common sense
is extracted from Large Language Models based on the current task at hand and
robot skills. For systematic evaluations, we collected a dataset that includes
561 execution-time situations in a dining domain, where each situation
corresponds to a state instance of a robot being potentially unable to complete
a task using a solution that normally works. Experimental results show that our
approach significantly outperforms competitive baselines from the literature in
the success rate of service tasks. Additionally, we have demonstrated COWP
using a mobile manipulator. Supplementary materials are available at:
https://cowplanning.github.io
Integrating Action Knowledge and LLMs for Task Planning and Situation Handling in Open Worlds
Task planning systems have been developed to help robots use human knowledge
(about actions) to complete long-horizon tasks. Most of them have been
developed for "closed worlds" while assuming the robot is provided with
complete world knowledge. However, the real world is generally open, and the
robots frequently encounter unforeseen situations that can potentially break
the planner's completeness. Could we leverage the recent advances on
pre-trained Large Language Models (LLMs) to enable classical planning systems
to deal with novel situations?
This paper introduces a novel framework, called COWP, for open-world task
planning and situation handling. COWP dynamically augments the robot's action
knowledge, including the preconditions and effects of actions, with
task-oriented commonsense knowledge. COWP embraces the openness from LLMs, and
is grounded to specific domains via action knowledge. For systematic
evaluations, we collected a dataset that includes 1,085 execution-time
situations. Each situation corresponds to a state instance wherein a robot is
potentially unable to complete a task using a solution that normally works.
Experimental results show that our approach outperforms competitive baselines
from the literature in the success rate of service tasks. Additionally, we have
demonstrated COWP using a mobile manipulator. Supplementary materials are
available at: https://cowplanning.github.io/Comment: arXiv admin note: substantial text overlap with arXiv:2210.0128
Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms
The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications
Adaptive Process Management in Cyber-Physical Domains
The increasing application of process-oriented approaches in new challenging cyber-physical domains beyond business computing (e.g., personalized healthcare, emergency management, factories of the future, home automation, etc.) has led to reconsider the level of flexibility and support required to manage complex processes in such domains. A cyber-physical domain is characterized by the presence of a cyber-physical system coordinating heterogeneous ICT components (PCs, smartphones, sensors, actuators) and involving real world entities (humans, machines, agents, robots, etc.) that perform complex tasks in the “physical” real world to achieve a common goal. The physical world, however, is not entirely predictable, and processes enacted in cyber-physical domains must be robust to unexpected conditions and adaptable to unanticipated exceptions. This demands a more flexible approach in process design and enactment, recognizing that in real-world environments it is not adequate to assume that all possible recovery activities can be predefined for dealing with the exceptions that can ensue. In this chapter, we tackle the above issue and we propose a general approach, a concrete framework and a process management system implementation, called SmartPM, for automatically adapting processes enacted in cyber-physical domains in case of unanticipated exceptions and exogenous events. The adaptation mechanism provided by SmartPM is based on declarative task specifications, execution monitoring for detecting failures and context changes at run-time, and automated planning techniques to self-repair the running process, without requiring to predefine any specific adaptation policy or exception handler at design-time
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