Robust Multi-Modal Policies for Industrial Assembly via Reinforcement Learning and Demonstrations: A Large-Scale Study (2103.11512v4)

Abstract: Over the past several years there has been a considerable research investment into learning-based approaches to industrial assembly, but despite significant progress these techniques have yet to be adopted by industry. We argue that it is the prohibitively large design space for Deep Reinforcement Learning (DRL), rather than algorithmic limitations per se, that are truly responsible for this lack of adoption. Pushing these techniques into the industrial mainstream requires an industry-oriented paradigm which differs significantly from the academic mindset. In this paper we define criteria for industry-oriented DRL, and perform a thorough comparison according to these criteria of one family of learning approaches, DRL from demonstration, against a professional industrial integrator on the recently established NIST assembly benchmark. We explain the design choices, representing several years of investigation, which enabled our DRL system to consistently outperform the integrator baseline in terms of both speed and reliability. Finally, we conclude with a competition between our DRL system and a human on a challenge task of insertion into a randomly moving target. This study suggests that DRL is capable of outperforming not only established engineered approaches, but the human motor system as well, and that there remains significant room for improvement. Videos can be found on our project website: https://sites.google.com/view/shield-nist.

Robust Multi-Modal Policies for Industrial Assembly: An Evaluation of Reinforcement Learning and Demonstrations

The paper "Robust Multi-Modal Policies for Industrial Assembly via Reinforcement Learning and Demonstrations: A Large-Scale Study" presents an empirical and methodological investigation into the application of Deep Reinforcement Learning (DRL) for industrial assembly tasks. This research, which aligns with interests in industrial automation, focuses on overcoming challenges posed by DRL in practical scenarios by incorporating demonstrations and systematic evaluations.

Introduction and Problem Definition

The paper sets out to address a critical barrier in adopting DRL techniques in industrial settings, emphasizing that the design space for DRL has been more of an impediment than algorithmic limitations. In response, the authors argue for a transition to industry-oriented DRL, proposing criteria such as efficiency, economy, and thorough evaluation. With this framework, they explore SHIELD, an adapted DDPGfD algorithm that integrates human demonstrations and off-policy corrections to surmount traditional constraints and complexities in industrial applications. The researchers articulate their approach against established methods on the NIST assembly benchmark, which provides a standardized metric for assessing robotic assembly tasks.

Key Contributions

The paper’s primary empirical contributions are extensive. It performs a large-scale systematic evaluation of the RL algorithm on a benchmark designed to emulate real-world industrial manipulation tasks. One of the standout results is that the learned DRL policies achieved a 99.8% success rate across 13,096 trials, indicating high reliability and robustness. This signifies the potential of DRL methods to offer solutions at par with professional integrators. Moreover, they present the SHIELD system competing against humans in insertion tasks into moving targets, demonstrating capabilities potentially exceeding human motor skills.

Methods and Evaluation

The methodological innovations introduced in the paper involve multiple enhancements of the DDPGfD algorithm. These include removing exploration noise, leveraging human demonstrations for system initialization, implementing on-policy corrections, and introducing curricular mechanisms for task and action space adaptation. Notably, the use of relative coordinates and goal randomization allowed for policy generalization beyond specified training conditions. In practical implementations, pre-trained visual features supported DRL agents in efficiently learning policies from complex sensory inputs, utilizing unsupervised learning objectives to improve sample efficiency.

Implications and Future Directions

This paper provides significant implications for both theoretical advancements and practical applications in the domain of industrial robotics. By highlighting how robust policies can be developed through learning from demonstrations coupled with strategic algorithmic design choices, it offers a compelling narrative for DRL adoption in fields heretofore reliant on traditional engineering solutions. The impact of successfully training robotic policies to outperform human capabilities and extend beyond conventional constraints points to promising developments in industrial automation.

Looking ahead, the authors point to areas where DRL approaches could unlock new applications, particularly in unconstrained environments. They advocate for scale-up evaluations to potentially achieve near-perfect reliability rates. Moreover, advancements in visual representation learning and the integration of offline RL paradigms could further enhance the accessibility and efficiency of DRL systems in industrial settings.

In conclusion, this paper presents a thorough evaluation of RL techniques tailored for industry use, advocating for their practical adoption and underscoring pathways for continued research and development. The paper’s insights pave the way for strategic incorporation of demonstrations and multi-modal learning in driving robotic automation towards higher performance thresholds.