Robotics Enabling the Workforce
- URL: http://arxiv.org/abs/2012.09309v1
- Date: Wed, 16 Dec 2020 23:05:10 GMT
- Title: Robotics Enabling the Workforce
- Authors: Henrik Christensen, Maria Gini, Odest Chadwicke Jenkins, and Holly
Yanco
- Abstract summary: We need to invest in basic research, technology development, K-16 education, and lifelong learning.
In order to make the U.S. a leader in robotics, we need to invest in basic research, technology development, K-16 education, and lifelong learning.
- Score: 5.0555627833288
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Robotics has the potential to magnify the skilled workforce of the nation by
complementing our workforce with automation: teams of people and robots will be
able to do more than either could alone. The economic engine of the U.S. runs
on the productivity of our people. The rise of automation offers new
opportunities to enhance the work of our citizens and drive the innovation and
prosperity of our industries. Most critically, we need research to understand
how future robot technologies can best complement our workforce to get the best
of both human and automated labor in a collaborative team. Investments made in
robotics research and workforce development will lead to increased GDP, an
increased export-import ratio, a growing middle class of skilled workers, and a
U.S.-based supply chain that can withstand global pandemics and other
disruptions. In order to make the United States a leader in robotics, we need
to invest in basic research, technology development, K-16 education, and
lifelong learning.
Related papers
- $π_0$: A Vision-Language-Action Flow Model for General Robot Control [77.32743739202543]
We propose a novel flow matching architecture built on top of a pre-trained vision-language model (VLM) to inherit Internet-scale semantic knowledge.
We evaluate our model in terms of its ability to perform tasks in zero shot after pre-training, follow language instructions from people, and its ability to acquire new skills via fine-tuning.
arXiv Detail & Related papers (2024-10-31T17:22:30Z) - Towards the Terminator Economy: Assessing Job Exposure to AI through LLMs [10.844598404826355]
One-third of U.S. employment is highly exposed to AI, primarily in high-skill jobs.
This exposure correlates positively with employment and wage growth from 2019 to 2023.
arXiv Detail & Related papers (2024-07-27T08:14:18Z) - Brief for the Canada House of Commons Study on the Implications of
Artificial Intelligence Technologies for the Canadian Labor Force: Generative
Artificial Intelligence Shatters Models of AI and Labor [1.0878040851638]
As with past technologies, generative AI may not lead to mass unemployment.
generative AI is creative, cognitive, and potentially ubiquitous.
As AI's full set of capabilities and applications emerge, policy makers should promote workers' career adaptability.
arXiv Detail & Related papers (2023-11-06T22:58:24Z) - 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) - HERD: Continuous Human-to-Robot Evolution for Learning from Human
Demonstration [57.045140028275036]
We show that manipulation skills can be transferred from a human to a robot through the use of micro-evolutionary reinforcement learning.
We propose an algorithm for multi-dimensional evolution path searching that allows joint optimization of both the robot evolution path and the policy.
arXiv Detail & Related papers (2022-12-08T15:56:13Z) - Fleet-DAgger: Interactive Robot Fleet Learning with Scalable Human
Supervision [72.4735163268491]
Commercial and industrial deployments of robot fleets often fall back on remote human teleoperators during execution.
We formalize the Interactive Fleet Learning (IFL) setting, in which multiple robots interactively query and learn from multiple human supervisors.
We propose Fleet-DAgger, a family of IFL algorithms, and compare a novel Fleet-DAgger algorithm to 4 baselines in simulation.
arXiv Detail & Related papers (2022-06-29T01:23:57Z) - The Turing Trap: The Promise & Peril of Human-Like Artificial
Intelligence [1.9143819780453073]
The benefits of human-like artificial intelligence include soaring productivity, increased leisure, and perhaps most profoundly, a better understanding of our own minds.
But not all types of AI are human-like. In fact, many of the most powerful systems are very different from humans.
As machines become better substitutes for human labor, workers lose economic and political bargaining power.
In contrast, when AI is focused on augmenting humans rather than mimicking them, then humans retain the power to insist on a share of the value created.
arXiv Detail & Related papers (2022-01-11T21:07:17Z) - AI and Shared Prosperity [0.0]
Future advances in AI that automate away human labor may have stark implications for labor markets and inequality.
This paper proposes a framework to analyze the effects of specific types of AI systems on the labor market, based on how much labor demand they will create versus displace.
arXiv Detail & Related papers (2021-05-18T12:37:18Z) - The Road to a Successful HRI: AI, Trust and ethicS-TRAITS [65.60507052509406]
The aim of this workshop is to give researchers from academia and industry the possibility to discuss the inter-and multi-disciplinary nature of the relationships between people and robots.
arXiv Detail & Related papers (2021-03-23T16:52:12Z) - Do's and Don'ts for Human and Digital Worker Integration [14.624340432672172]
We argue for a broader view that incorporates the potential for multiple levels of autonomy and human involvement.
We argue for a wider range of metrics beyond productivity when integrating digital workers into a business process.
arXiv Detail & Related papers (2020-10-15T13:30:23Z) - OpenBot: Turning Smartphones into Robots [95.94432031144716]
Current robots are either expensive or make significant compromises on sensory richness, computational power, and communication capabilities.
We propose to leverage smartphones to equip robots with extensive sensor suites, powerful computational abilities, state-of-the-art communication channels, and access to a thriving software ecosystem.
We design a small electric vehicle that costs $50 and serves as a robot body for standard Android smartphones.
arXiv Detail & Related papers (2020-08-24T18:04:50Z)
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.