A Study of the Efficacy of Generative Flow Networks for Robotics and Machine Fault-Adaptation

TL;DR

This work investigates fault adaptation in robotics under out-of-distribution conditions by evaluating Continuous Flow Networks (CFlowNets) against established RL baselines (DDPG, TD3, PPO, SAC) in a Reacher-v2 environment with four simulated faults. The authors adapt GFlowNets to continuous control (CFlowNets), implement a three-stage protocol (pre-fault learning, fault injection, post-fault adaptation), and compare adaptation speed, sample efficiency, and asymptotic performance, including transfer of pre-fault knowledge. Results show that CFlowNets often achieve faster adaptation with competitive or superior asymptotic performance but at substantial compute and GPU memory cost, while PPO offers strong performance with much lower resource demands. The findings suggest CFlowNets as a promising framework for rapid fault adaptation in robotics, with practical deployment considerations and avenues for future work in more complex 3D tasks and multi-fault scenarios.

Abstract

Advancements in robotics have opened possibilities to automate tasks in various fields such as manufacturing, emergency response and healthcare. However, a significant challenge that prevents robots from operating in real-world environments effectively is out-of-distribution (OOD) situations, wherein robots encounter unforseen situations. One major OOD situations is when robots encounter faults, making fault adaptation essential for real-world operation for robots. Current state-of-the-art reinforcement learning algorithms show promising results but suffer from sample inefficiency, leading to low adaptation speed due to their limited ability to generalize to OOD situations. Our research is a step towards adding hardware fault tolerance and fast fault adaptability to machines. In this research, our primary focus is to investigate the efficacy of generative flow networks in robotic environments, particularly in the domain of machine fault adaptation. We simulated a robotic environment called Reacher in our experiments. We modify this environment to introduce four distinct fault environments that replicate real-world machines/robot malfunctions. The empirical evaluation of this research indicates that continuous generative flow networks (CFlowNets) indeed have the capability to add adaptive behaviors in machines under adversarial conditions. Furthermore, the comparative analysis of CFlowNets with reinforcement learning algorithms also provides some key insights into the performance in terms of adaptation speed and sample efficiency. Additionally, a separate study investigates the implications of transferring knowledge from pre-fault task to post-fault environments. Our experiments confirm that CFlowNets has the potential to be deployed in a real-world machine and it can demonstrate adaptability in case of malfunctions to maintain functionality.

Figures (11)

• Figure 1: Flow Network DAG Illustration based on bengio_malkin_jain_2022.
• Figure 2: GFlowNets Architecture based on bengio_malkin_jain_2022
• Figure 3: Schamatics of CFlowNets Training Framework li2023cflownets. The leftmost part represents the action selection procedure. The middle part is the flow-matching approximation visualization and the rightmost section shows the Continuous Flow-Matching Loss, which is utilized for training.
• Figure 4: An Overview of Our Experimental Setup.
• Figure 5: The early performance in Motion Impairment Fault environments is depicted through learning curves for all five algorithms. The dashed line represents the asymptotic performance.