A Roadmap Towards Automated and Regulated Robotic Systems
- URL: http://arxiv.org/abs/2403.14049v1
- Date: Thu, 21 Mar 2024 00:14:53 GMT
- Title: A Roadmap Towards Automated and Regulated Robotic Systems
- Authors: Yihao Liu, Mehran Armand,
- Abstract summary: We argue that the unregulated generative processes from AI is fitted for low level end tasks.
We propose a roadmap that can lead to fully automated and regulated robotic systems.
- Score: 4.6015001632772545
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The rapid development of generative technology opens up possibility for higher level of automation, and artificial intelligence (AI) embodiment in robotic systems is imminent. However, due to the blackbox nature of the generative technology, the generation of the knowledge and workflow scheme is uncontrolled, especially in a dynamic environment and a complex scene. This poses challenges to regulations in safety-demanding applications such as medical scenes. We argue that the unregulated generative processes from AI is fitted for low level end tasks, but intervention in the form of manual or automated regulation should happen post-workflow-generation and pre-robotic-execution. To address this, we propose a roadmap that can lead to fully automated and regulated robotic systems. In this paradigm, the high level policies are generated as structured graph data, enabling regulatory oversight and reusability, while the code base for lower level tasks is generated by generative models. Our approach aims the transitioning from expert knowledge to regulated action, akin to the iterative processes of study, practice, scrutiny, and execution in human tasks. We identify the generative and deterministic processes in a design cycle, where generative processes serve as a text-based world simulator and the deterministic processes generate the executable system. We propose State Machine Seralization Language (SMSL) to be the conversion point between text simulator and executable workflow control. From there, we analyze the modules involved based on the current literature, and discuss human in the loop. As a roadmap, this work identifies the current possible implementation and future work. This work does not provide an implemented system but envisions to inspire the researchers working on the direction in the roadmap. We implement the SMSL and D-SFO paradigm that serve as the starting point of the roadmap.
Related papers
- Towards Single-System Illusion in Software-Defined Vehicles -- Automated, AI-Powered Workflow [3.2821049498759094]
We propose a novel model- and feature-based approach to development of vehicle software systems.
One of the key points of the presented approach is the inclusion of modern generative AI, specifically Large Language Models (LLMs)
The resulting pipeline is automated to a large extent, with feedback being generated at each step.
arXiv Detail & Related papers (2024-03-21T15:07:57Z) - RoboScript: Code Generation for Free-Form Manipulation Tasks across Real
and Simulation [77.41969287400977]
This paper presents textbfRobotScript, a platform for a deployable robot manipulation pipeline powered by code generation.
We also present a benchmark for a code generation benchmark for robot manipulation tasks in free-form natural language.
We demonstrate the adaptability of our code generation framework across multiple robot embodiments, including the Franka and UR5 robot arms.
arXiv Detail & Related papers (2024-02-22T15:12:00Z) - InCoRo: In-Context Learning for Robotics Control with Feedback Loops [4.702566749969133]
InCoRo is a system that uses a classical robotic feedback loop composed of an LLM controller, a scene understanding unit, and a robot.
We highlight the generalization capabilities of our system and show that InCoRo surpasses the prior art in terms of the success rate.
This research paves the way towards building reliable, efficient, intelligent autonomous systems that adapt to dynamic environments.
arXiv Detail & Related papers (2024-02-07T19:01:11Z) - The Foundations of Computational Management: A Systematic Approach to
Task Automation for the Integration of Artificial Intelligence into Existing
Workflows [55.2480439325792]
This article introduces Computational Management, a systematic approach to task automation.
The article offers three easy step-by-step procedures to begin the process of implementing AI within a workflow.
arXiv Detail & Related papers (2024-02-07T01:45:14Z) - Automated Process Planning Based on a Semantic Capability Model and SMT [50.76251195257306]
In research of manufacturing systems and autonomous robots, the term capability is used for a machine-interpretable specification of a system function.
We present an approach that combines these two topics: starting from a semantic capability model, an AI planning problem is automatically generated.
arXiv Detail & Related papers (2023-12-14T10:37:34Z) - RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation [68.70755196744533]
RoboGen is a generative robotic agent that automatically learns diverse robotic skills at scale via generative simulation.
Our work attempts to extract the extensive and versatile knowledge embedded in large-scale models and transfer them to the field of robotics.
arXiv Detail & Related papers (2023-11-02T17:59:21Z) - ProAgent: From Robotic Process Automation to Agentic Process Automation [87.0555252338361]
Large Language Models (LLMs) have emerged human-like intelligence.
This paper introduces Agentic Process Automation (APA), a groundbreaking automation paradigm using LLM-based agents for advanced automation.
We then instantiate ProAgent, an agent designed to craft from human instructions and make intricate decisions by coordinating specialized agents.
arXiv Detail & Related papers (2023-11-02T14:32:16Z) - ProgPrompt: Generating Situated Robot Task Plans using Large Language
Models [68.57918965060787]
Large language models (LLMs) can be used to score potential next actions during task planning.
We present a programmatic LLM prompt structure that enables plan generation functional across situated environments.
arXiv Detail & Related papers (2022-09-22T20:29:49Z) - Robotic Process Automation Using Process Mining $\unicode{x2013}$ A
Systematic Literature Review [0.7252027234425332]
This paper aims to assess the applicability of process mining in accelerating and improving the implementation of RPA.
A systematic literature review was conducted to examine the approaches where PM techniques were used to understand the as-is processes that can be automated with software robots.
There is a steady increase in the number of publications in this domain, especially during the year 2022, which suggests a raising interest in the combined use of PM with RPA.
arXiv Detail & Related papers (2022-04-02T03:13:17Z) - Towards Intelligent Robotic Process Automation for BPMers [0.8701566919381222]
Robotic Process Automation (RPA) is a fast-emerging automation technology that sits between the fields of Business Process Management (BPM) and Artificial Intelligence (AI)
RPA tools are able to capture the execution of such routines previously performed by a human users on the interface of a computer system, and then emulate their enactment in place of the user by means of a software robot.
arXiv Detail & Related papers (2020-01-03T12:37:52Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.