Reverse autonomous learning: A new chain of command

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Reverse autonomous learning: A new chain of command

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Reverse autonomous learning: A new chain of command

Subheading text
Cobots that learn from humans are reshaping the future of supply chains and beyond.
    • Author:
    • Author name
      Quantumrun Foresight
    • August 29, 2023

    Insight highlights

    As we march into the era of automation, cobots leveraging inverse reinforcement learning (IRL) are redefining the landscape of supply chains. By observing and learning from human tasks, cobots are not only increasing productivity but also shaping the workforce of the future, opening up opportunities for skill advancement and innovation across industries. However, as this technological tide surges, it brings a slew of societal changes from shifting demographics to new labor laws, urban planning needs, and mental health concerns, underscoring the need for a balanced approach.

    Reverse autonomous learning context

    Conventional reinforcement learning (RL) aims to develop a decision-making process that optimizes a predetermined reward function. However, inverse reinforcement learning (IRL), a concept introduced by Andrew Ng and Stuart Russell in 2000, reverses this approach, aiming to deduce the reward function from an agent's exhibited behavior. At the core, IRL, apprenticeship learning, and similar imitation learning methods are successful due to their ability to harness insights from a policy executed by a human expert. Nevertheless, the ultimate aspiration is to enable machine learning (ML) systems to learn from diverse human data.

    Cobots or "collaborative robots" are leveraging IRL to enhance human productivity and safety in supply chains. With the ability to learn by watching human workers, cobots can replicate human motions and improve efficiency, eliminating redundant tasks. This transformative technology is making inroads into the autonomous trucking industry, demonstrated by Plus, a company utilizing artificial intelligence (AI) to learn and adapt to unexpected road conditions, fostering safer, autonomous transportation. As AI/ML platforms amass billions of miles of driving data, Plus aims to apply this information in a fully driverless environment. 

    In parallel, the Robotics Institute at Carnegie Mellon University is pioneering Wild Human-Imitated Robot Learning, enabling robots to learn from online video footage. Companies like Brooks, with its robot assistant Carter, are employing these advancements to develop automatons capable of closely observing and assisting human tasks, signaling a monumental shift in the way supply chains operate.

    Disruptive impact

    Cobots using IRL may offer considerable opportunities for skill advancement and professional development. As cobots are designed to observe and learn from human tasks, they inherently allow a smoother integration into existing work environments. Over time, this could lead to a shift in job roles, where individuals will spend less time on mundane tasks and more on critical thinking and decision-making roles. The cobot-human partnership could provide individuals with opportunities to upskill, focusing more on managing and maintaining these automated systems, creating an ecosystem of operators.

    Moreover, startups could capitalize on this trend by offering unique solutions related to the integration, maintenance, and improvement of cobots. For instance, they could develop advanced programming interfaces, create more intuitive human-machine interaction models, or even establish training programs for workers adapting to these new systems. They could also address the ethical and safety concerns that may arise from widespread cobot implementation, offering solutions that align with regulatory norms while enhancing operational efficiency.

    Companies could leverage this technology to drive efficiency, reduce costs, and improve workplace safety. However, the transition may require substantial investments in infrastructure and worker training. Meanwhile, governments may need to balance fostering innovation and ensuring worker safety and job security, possibly leading to new regulatory frameworks around labor laws and workplace standards. 

    Implications of reverse autonomous learning

    Wider implications of reverse autonomous learning may include: 

    • Legislators facing pressure to balance between protecting jobs and supporting technological progress. There may also be revisions in labor laws, such as those related to work hours, health, safety, and compensation, given that cobots can work around the clock without the need for breaks.
    • More technically skilled workers whose fundamental roles are to train, operate, and maintain robots.
    • Reduced environmental footprint by optimizing resource use and reducing waste. However, concerns about energy consumption and the disposal of obsolete models may arise.
    • Educational institutions needing to overhaul their curriculum to prepare students for the changing job market. Vocational training programs could become more focused on robotics and AI.
    • Cities adapting their infrastructure to accommodate the increased use of cobots. For example, roads and transport systems may need to accommodate autonomous vehicles in the supply chain industry.
    • Supply chains becoming heavily reliant on this technology, making them vulnerable to technical failures or cyber-attacks.
    • A more automated workplace creating stress and anxiety among workers about job security, leading to potential implications for societal mental health and welfare support systems.
    • Decreased human-human interaction, which could impact workplace culture and teamwork.

    Questions to consider

    • If you work in a supply chain, how is your company adopting cobots?
    • How might supply chains collaborate with their human workers to ensure they work well with cobots?