APPLI: Adaptive Planner Parameter Learning From Interventions (2011.00400v2)

Abstract: While classical autonomous navigation systems can typically move robots from one point to another safely and in a collision-free manner, these systems may fail or produce suboptimal behavior in certain scenarios. The current practice in such scenarios is to manually re-tune the system's parameters, e.g. max speed, sampling rate, inflation radius, to optimize performance. This practice requires expert knowledge and may jeopardize performance in the originally good scenarios. Meanwhile, it is relatively easy for a human to identify those failure or suboptimal cases and provide a teleoperated intervention to correct the failure or suboptimal behavior. In this work, we seek to learn from those human interventions to improve navigation performance. In particular, we propose Adaptive Planner Parameter Learning from Interventions (APPLI), in which multiple sets of navigation parameters are learned during training and applied based on a confidence measure to the underlying navigation system during deployment. In our physical experiments, the robot achieves better performance compared to the planner with static default parameters, and even dynamic parameters learned from a full human demonstration. We also show APPLI's generalizability in another unseen physical test course, and a suite of 300 simulated navigation environments.

• The paper introduces a method that learns navigation parameters from human interventions to dynamically optimize autonomous robot performance.
• It employs a confidence measure via Evidential Deep Learning to determine when to apply learned parameters instead of static defaults.
• Empirical results demonstrate reduced traversal times and fewer failures in both real-world and simulated test environments.

Adaptive Planner Parameter Learning from Interventions: An Overview

The paper "APPLI: Adaptive Planner Parameter Learning From Interventions" presents a novel approach to improving autonomous mobile robot navigation by leveraging human interventions. This research addresses a critical issue prevalent in classical autonomous navigation systems, which involve tuning parameters manually in scenarios where these systems fail or perform suboptimally. By introducing Adaptive Planner Parameter Learning from Interventions (APPLI), this paper aims to optimize robot navigation adaptively through learned parameter sets derived from human interventions.

The research identifies two major types of interventions when robots fail during navigation: Type A interventions, which address severe navigation failures, and Type B interventions, which aim to rectify suboptimal performance. Using these interventions, the paper proposes to learn dynamic navigation parameters that can replace static default parameters, therefore optimizing navigation in various scenarios dynamically based on a confidence measure.

Key Contributions

1. Parameter Learning from Human Interventions: APPLI learns from intervention-based telemetry, which allows the system to correct both failure cases and suboptimal behaviors by dynamically applying learned parameters. This technique effortlessly integrates into existing planners like the Dynamic Window Approach (DWA), enhancing their versatility without overhauling the entire system.
2. Confidence-based Parameter Application: The introduction of a confidence measure with Evidential Deep Learning (EDL) determines when to apply learned parameters versus default ones, ensuring the autonomous robot retains optimal performance even in environments not seen during training.
3. Empirical Validation: Through rigorous physical and simulated experiments, the research demonstrates the viability of APPLI in real-world and unseen environments. It particularly highlights the improved performance—shorter traversal times and reduced failure rates—compared to traditional methods with static parameters.

Numerical Results and Implications

Experiments conducted in physical test environments using a ClearPath Jackal robot showed that APPLI significantly reduced traversal time compared to a system using static, manually-tuned parameters. When applied in simulated environments, APPLI demonstrated superior performance across a wide range of scenarios, indicating its potential for generalization across diverse real-world situations.

The speculative implications of this research suggest a shifting paradigm in robot path planning. The reliance on human interventions to automatically adjust algorithmic parameters could lead to more adaptive and responsive navigation systems. Additionally, using a confidence-based decision framework ensures these enhanced systems are more reliable in dynamic and unpredictable environments.

Future Developments

Future advancements could involve integrating a broader spectrum of intervention types, such as non-sequential intervention data, to further refine the learning process of navigation parameters. Extending the methodology to other robotic systems or incorporating other sensors could potentially validate the generalizability of APPLI across different robotic platforms. Moreover, exploring more sophisticated machine learning models to enhance confidence measurement might improve the prediction accuracy of parameter application, thereby further optimizing the navigation performance.

In conclusion, APPLI stands as a significant contribution towards adaptive autonomous navigation—the methodology not only uplifts current navigation capabilities but also sets a foundation for future research to develop more intelligent and adaptive robotic systems.