Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewards (1906.05841v2)

Abstract: Connector insertion and many other tasks commonly found in modern manufacturing settings involve complex contact dynamics and friction. Since it is difficult to capture related physical effects with first-order modeling, traditional control methods often result in brittle and inaccurate controllers, which have to be manually tuned. Reinforcement learning (RL) methods have been demonstrated to be capable of learning controllers in such environments from autonomous interaction with the environment, but running RL algorithms in the real world poses sample efficiency and safety challenges. Moreover, in practical real-world settings we cannot assume access to perfect state information or dense reward signals. In this paper, we consider a variety of difficult industrial insertion tasks with visual inputs and different natural reward specifications, namely sparse rewards and goal images. We show that methods that combine RL with prior information, such as classical controllers or demonstrations, can solve these tasks from a reasonable amount of real-world interaction.

Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewards

This research paper addresses the application of deep reinforcement learning (RL) techniques in complex industrial automation tasks, specifically focusing on connector insertion tasks. The challenge tackled is significant due to the complexity of contact dynamics and friction which are difficult to model analytically, often resulting in brittle controllers when traditional control methods are used. Automation in such domains demands systems capable of adaptive, precise control while handling visual input and sparse reward conditions.

Key Contributions and Methodologies

1. Task Contextualization: The tasks studied involve inserting various types of connectors, such as USB, D-Sub, and Model-E, each presenting unique challenges such as precision in alignment and variable frictional forces. The difficulty is further exacerbated by variations in real-world conditions such as sensor noise and environmental disturbances.
2. RL with Visual Input and Sparse Rewards: Reinforcement learning serves as the backbone of the proposed solution, leveraging visual inputs and natural rewards. The latter form part of the core challenges in this paper, given the reliance on sparse binary signals indicating task success, in contrast to more engineered dense rewards. The approach also extends to using input images as both state representations and goal specifications.
3. Effective Algorithms: The research evaluates RL algorithms such as Twin Delayed Deep Deterministic Policy Gradients (TD3) and Soft Actor Critic (SAC) due to their sample-efficient characteristics and robustness. These algorithms are supplemented with residual RL approaches which incorporate prior knowledge in the form of hand-crafted policies to optimize exploration and stability in learning.
4. Residual and Demonstration-enhanced Learning: To improve sample efficiency and safety in learned policies, the paper explores residual RL where a parametric policy is superimposed on a base hand-crafted controller. Additionally, learning from demonstrations is utilized to further refine the policy by capturing expert-like actions effectively.

Results

The experiments conducted on a Sawyer robotic arm demonstrate the efficacy of policies trained with these RL methods. When trained with vision-based inputs, the residual RL methods successfully adapt despite significant noise and lack of prior state information, outperforming traditional controllers in terms of robustness and reliability.

For tasks where detailed state information is available, the shaped dense rewards allowed further optimization, yet interestingly, the natural and sparse reward-driven approaches achieved competent performance, emphasizing the potential of these less engineered methods in practical applications.

Implications and Future Directions

The implications of this paper highlight a promising application of deep RL in industrial settings where traditional modeling approaches fall short. The ability to train policies that can generalize under varied conditions aligns with the objective of flexible robotic automation.

Future advancements could include extending this framework to tackle multi-stage assembly tasks, potentially incorporating hierarchical RL structures to handle increased complexity. There is also scope for exploring deeper integration with vision systems for better feature learning and environment interaction.

Overall, this work lays foundational steps towards democratizing automation solutions via RL, especially in environments demanding dexterous manipulation and adaptive control strategies.