APPLD: Adaptive Planner Parameter Learning from Demonstration (2004.00116v4)

Abstract: Existing autonomous robot navigation systems allow robots to move from one point to another in a collision-free manner. However, when facing new environments, these systems generally require re-tuning by expert roboticists with a good understanding of the inner workings of the navigation system. In contrast, even users who are unversed in the details of robot navigation algorithms can generate desirable navigation behavior in new environments via teleoperation. In this paper, we introduce APPLD, Adaptive Planner Parameter Learning from Demonstration, that allows existing navigation systems to be successfully applied to new complex environments, given only a human teleoperated demonstration of desirable navigation. APPLD is verified on two robots running different navigation systems in different environments. Experimental results show that APPLD can outperform navigation systems with the default and expert-tuned parameters, and even the human demonstrator themselves.

• The paper introduces APPLD, a method that adaptively tunes navigation parameters from human demonstrations to enhance robotic autonomy.
• It segments teleoperated demonstrations using change-point detection and employs behavior cloning to replicate effective navigation behaviors.
• Experimental results on platforms like Clearpath Jackal and BWIBot demonstrate APPLD's superior efficiency and adaptability in complex settings.

Overview of APPLD: Adaptive Planner Parameter Learning from Demonstration

The paper introduces APPLD, standing for Adaptive Planner Parameter Learning from Demonstration, an innovative approach designed to enhance autonomous robot navigation systems. The core issue that APPLD seeks to address is the need for re-tuning navigation systems when robots face new, complex environments. The traditional re-tuning process often requires expert intervention, which underscores the challenge faced by non-experts when attempting to adjust navigation parameters effectively.

Methodology and Approach

APPLD leverages a demonstration-based learning paradigm where a human operates the robot in the target environment, providing a demonstration through teleoperation. The method capitalizes on humans' natural ability to navigate various environments effectively, even without an intricate understanding of the robot's navigation system. This demonstration is then used as a basis to learn adaptive parameters that tailor the existing navigation system for optimal performance in new settings, facilitating a more autonomous approach to deployment in unfamiliar territories.

The approach involves several key steps:

1. Demonstration Segmentation: Utilizing change-point detection algorithms, the system segments the teleoperated demonstration into distinct contexts. Each context corresponds to homogeneous conditions where uniform navigation behavior can be observed.
2. Parameter Learning: For each identified context, APPLD employs behavior cloning techniques to determine the navigation parameters that best replicate the demonstrated behavior. The strategy is grounded in matching the demonstrated actions with those produced by the navigation system when simulated under various parameter configurations.
3. Online Context Prediction: The system deploys a neural network to classify and identify contexts in real-time based on sensory input, allowing it to dynamically switch navigation parameters as the robot encounters different sections of an environment.

Experimental Validation

The efficacy of APPLD is experimentally validated on two platforms: a Clearpath Jackal utilizing Dynamic Window Approach (DWA) and a BWIBot employing an elastic bands (e-band) technique. In each case, APPLD demonstrated superior performance compared to both the default parameter settings and manually expert-tuned configurations. Notably, in trials such as those involving the Jackal navigating a complex maze, APPLD not only exceeded the efficiency of expert-tuned parameters but also outperformed the human demonstrator in terms of traversal time, showcasing the system's ability to leverage learned parameters effectively.

Implications and Future Work

The findings presented in the paper suggest several significant implications for robot navigation:

• Practical Impact: By reducing the dependence on expert tuning, APPLD empowers end-users, potentially broadening the application range of robotic systems in real-world scenarios where environments are dynamic or complex.
• Theoretical Contributions: The method highlights the robustness of combining learning from demonstration with real-time adaptability, contributing to the understanding of parameter tuning and contextual adaptation in robotics.
• Future Prospects: The paper suggests potential avenues for future research, such as integrating clustering methods to accelerate context recognition and parameter learning. Additionally, there is scope for investigating joint learning approaches that intertwine segment identification and parameter optimization.

In summary, APPLD represents a significant advancement in the arena of autonomous navigation, bridging the gap between expert-intensive tuning processes and user-friendly deployment of robotic systems. Through empirical validation, the paper provides insights into the practical effectiveness of learning adaptive planning parameters, contributing meaningfully to the field's ongoing discourse on autonomous adaptation to environmental changes.