Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight (1902.03701v1)

Abstract: Deep reinforcement learning provides a promising approach for vision-based control of real-world robots. However, the generalization of such models depends critically on the quantity and variety of data available for training. This data can be difficult to obtain for some types of robotic systems, such as fragile, small-scale quadrotors. Simulated rendering and physics can provide for much larger datasets, but such data is inherently of lower quality: many of the phenomena that make the real-world autonomous flight problem challenging, such as complex physics and air currents, are modeled poorly or not at all, and the systematic differences between simulation and the real world are typically impossible to eliminate. In this work, we investigate how data from both simulation and the real world can be combined in a hybrid deep reinforcement learning algorithm. Our method uses real-world data to learn about the dynamics of the system, and simulated data to learn a generalizable perception system that can enable the robot to avoid collisions using only a monocular camera. We demonstrate our approach on a real-world nano aerial vehicle collision avoidance task, showing that with only an hour of real-world data, the quadrotor can avoid collisions in new environments with various lighting conditions and geometry. Code, instructions for building the aerial vehicles, and videos of the experiments can be found at github.com/gkahn13/GtS

• The paper introduces a hybrid DRL method that decouples visual perception learned in simulation from control policy trained with real-world data.
• It achieves robust collision avoidance, enabling nano aerial vehicles to fly four times further than baseline methods in diverse settings.
• The study demonstrates how combining simulated and real data minimizes training costs and improves generalization for autonomous flight systems.

Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight

This paper explores the integration of simulated and real-world data within deep reinforcement learning (DRL) frameworks to enhance generalization capabilities for vision-based autonomous flight, particularly focusing on collision avoidance tasks for nano aerial vehicles (NAVs). The authors propose a hybrid learning approach that simultaneously leverages the advantages of both data sources: simulation for learning generalizable visual features and real-world data for accurate dynamics modeling.

Methodological Approach

The paper introduces a method termed "generalization through simulation" (GtS). The approach consists of two main components: the perception subsystem and the control subsystem, which are trained using simulated and real-world data, respectively. The perception subsystem utilizes task-specific convolutional layers learned through reinforcement learning within simulated environments to develop a generalizable visual representation. Meanwhile, the control subsystem leverages real-world data to understand the NAV's dynamics for policy learning. By decoupling perception from control, the GtS method maximally exploits the domain-specific strengths of the simulated environment (visual generalization) and real-world environment (accurate dynamics modeling).

Experimental Evaluation

The authors conduct experiments on a real-world collision avoidance task using a Crazyflie 2.0 NAV equipped with a monocular camera. The task is evaluated across various hallway configurations to test generalization and robustness. Remarkably, the NAV exhibits robust performance, navigating challenging environments unseen during training by combining just an hour of real-world data with a substantial volume of simulated data. The GtS method outperforms several baseline approaches, including policies trained purely on simulated or real-world data and those employing alternative transfer learning techniques.

Numerical Results and Claims

Quantitatively, the authors report that the proposed method achieves a significant improvement in task performance, with the NAV flying four times further on average compared to other strategies in experiments challenging both perception and control robustness. This substantial improvement underscores the effectiveness of integrating a task-specific perception model learned in simulated environments with a goal-oriented control strategy based on real-world interaction data.

Implications and Future Directions

The hybrid approach delineated in this paper has significant implications for advancing the generalizability of reinforcement learning models in robotics. By distinctly leveraging the strengths of simulated and real-world domains, the method provides a principled framework for deploying DRL on SWaP-constrained platforms where data acquisition costs are substantial. For future developments, the paper suggests integrating simulated and real-world data collection more dynamically to foster an active learning loop that optimally balances simulated and real-world exploration, potentially enhancing the efficiency of data-driven modeling even further.

Conclusion

The paper contributes to the broader discourse on transfer learning and domain adaptation in autonomous systems, demonstrating how domain-specific features and dynamics modeling can be decoupled to yield superior task performance. This approach, while applied here in the context of NAV navigation, could easily extend to other robotics and control systems, setting a foundation for further advancements in simulation-to-reality transfer learning technologies. Potential future work could focus on refining these techniques to achieve even more effective and computationally efficient solutions in increasingly diversified task environments.