Improving Autonomous Racing Performance by Learning from Multiple Imperfect Experts

Kernekoncepter

MEGA-DAgger, a new DAgger variant, can effectively learn from multiple imperfect experts to achieve better-than-experts performance in autonomous racing.

Resumé

The paper proposes MEGA-DAgger, a new DAgger variant for interactive imitation learning from multiple imperfect experts. The key challenges addressed are:

Unsafe demonstrations from imperfect experts: MEGA-DAgger uses a data filter based on Control Barrier Functions to remove unsafe demonstrations during data aggregation.

Conflicting demonstration labels from different experts: MEGA-DAgger employs a conflict resolution mechanism that evaluates and selects the best expert action based on safety and progress scores.

Through experiments in autonomous racing scenarios, the authors demonstrate that the policy learned using MEGA-DAgger can outperform both the individual experts and policies learned using the state-of-the-art interactive imitation learning algorithm, Human-Gated DAgger (HG-DAgger).
The key findings are:

The data filter significantly improves safety by removing unsafe demonstrations, especially as the probability of undesired behaviors from experts increases.

The conflict resolution mechanism allows MEGA-DAgger to effectively leverage complementary good demonstrations from different experts, leading to a better-than-experts policy.

Extensive experiments on different maps show MEGA-DAgger achieves about 45% average improvement on both overtaking and collision avoidance compared to HG-DAgger.

Real-world experiments on the F1TENTH autonomous racing platform demonstrate the effectiveness of MEGA-DAgger in bridging the sim-to-real gap.

Statistik

The percentage of collisions for the MEGA-DAgger policy is 21.2% ± 1.9%, which is 13.6% better than the cumulative performance of all experts.
The percentage of overtakes for the MEGA-DAgger policy is 78.1% ± 1.6%, which is 13.2% better than the cumulative performance of all experts.

Citater

"MEGA-DAgger has about 45% average improvement on both overtaking and collision avoidance compared with vanilla HG-DAgger, and has about 15% average improvement compared with HG-DAgger with data filter."
"We empirically attribute the improved performance of MEGA-DAgger over HG-DAgger with data filter to learning from complementary good demonstrations from different experts."

Vigtigste indsigter udtrukket fra

MEGA-DAgger: Imitation Learning with Multiple Imperfect Experts

by Xiatao Sun,S... kl. arxiv.org 05-03-2024

https://arxiv.org/pdf/2303.00638.pdf

Dybere Forespørgsler

How can the conflict resolution mechanism be further improved to better leverage the strengths of different experts?

In order to enhance the conflict resolution mechanism in MEGA-DAgger to better leverage the strengths of different experts, several strategies can be implemented:

Dynamic Weighting: Instead of simply choosing the action label of the expert with the highest evaluation score, a dynamic weighting scheme can be introduced. This scheme could assign different weights to the actions of each expert based on their historical performance or reliability. By dynamically adjusting these weights during the conflict resolution process, the mechanism can adapt to the changing expertise levels of the experts.

Ensemble Methods: Implementing ensemble methods can help combine the predictions of multiple experts in a more sophisticated manner. Techniques like bagging or boosting can be utilized to aggregate the decisions of different experts and create a more robust final action label. This can help in reducing the impact of individual expert errors and biases.

Adaptive Similarity Threshold: Instead of using a fixed cosine similarity threshold to identify similar observations, an adaptive threshold based on the difficulty of the scenario or the diversity of expert actions can be employed. This adaptive threshold can ensure that conflicts are resolved more effectively in challenging situations where experts may have varying opinions.

Feedback Loop: Introducing a feedback loop mechanism where the performance of the learned policy is used to provide feedback to the conflict resolution process can be beneficial. By analyzing the outcomes of the policy's actions and feeding this information back into the conflict resolution mechanism, adjustments can be made to improve the selection of expert actions in future iterations.

How can the sim-to-real gap be further reduced when deploying MEGA-DAgger on real-world autonomous vehicles?

Reducing the sim-to-real gap when deploying MEGA-DAgger on real-world autonomous vehicles requires careful consideration and implementation of the following strategies:

Domain Randomization: Implementing domain randomization techniques during training can help the learned policy generalize better to real-world scenarios. By introducing variations in environmental factors such as lighting conditions, textures, and object placements, the policy can learn to adapt to a wide range of conditions.

Transfer Learning: Utilizing transfer learning methods can facilitate the adaptation of the learned policy from simulation to the real world. By fine-tuning the policy on real-world data after initial training in simulation, the model can quickly adjust to the nuances and complexities of the real environment.

Sensor Fidelity Matching: Ensuring that the sensors used in simulation closely match those in the real-world setup is crucial for reducing the sim-to-real gap. Calibrating the simulation sensors to mimic the noise, resolution, and characteristics of real sensors can improve the policy's performance when deployed on actual vehicles.

Realistic Simulation: Enhancing the realism of the simulation environment by incorporating realistic physics, dynamics, and sensor models can bridge the sim-to-real gap. Accurate representations of real-world dynamics in simulation can better prepare the learned policy for the challenges it will face in the physical world.

Continuous Evaluation and Iteration: Regularly evaluating the policy's performance in real-world scenarios and iteratively refining the training process based on this feedback is essential. Continuous learning and adaptation based on real-world data can significantly reduce the sim-to-real gap over time.

What other applications beyond autonomous racing could benefit from the MEGA-DAgger framework for learning from multiple imperfect experts?

The MEGA-DAgger framework for learning from multiple imperfect experts can be applied to various domains beyond autonomous racing, including:

Medical Robotics: In surgical robotics, where expert surgeons may have different techniques and approaches, MEGA-DAgger can help train robotic systems to perform complex surgical tasks by learning from a diverse set of expert demonstrations.

Industrial Automation: In manufacturing settings, where multiple operators may have varying strategies for optimizing production processes, MEGA-DAgger can be utilized to train robotic systems to perform tasks efficiently by leveraging the expertise of different operators.

Financial Trading: In algorithmic trading, where traders may have different trading strategies and risk preferences, MEGA-DAgger can assist in training trading algorithms to make informed decisions by learning from a range of expert behaviors.

Natural Language Processing: In language generation tasks, where multiple language experts may have different writing styles and preferences, MEGA-DAgger can be employed to train language models to generate diverse and contextually appropriate text by incorporating insights from various experts.

Autonomous Navigation: In unmanned aerial vehicles (UAVs) or autonomous marine vehicles, where expert pilots or captains may have different navigation techniques, MEGA-DAgger can aid in training autonomous systems to navigate complex environments by learning from a mix of expert demonstrations.

By applying the MEGA-DAgger framework to these diverse domains, it is possible to leverage the collective knowledge and expertise of multiple imperfect experts to enhance the learning process and improve the performance of autonomous systems in various real-world applications.