APPLE: Adaptive Planner Parameter Learning from Evaluative Feedback (2108.09801v1)

Abstract: Classical autonomous navigation systems can control robots in a collision-free manner, oftentimes with verifiable safety and explainability. When facing new environments, however, fine-tuning of the system parameters by an expert is typically required before the system can navigate as expected. To alleviate this requirement, the recently-proposed Adaptive Planner Parameter Learning paradigm allows robots to \emph{learn} how to dynamically adjust planner parameters using a teleoperated demonstration or corrective interventions from non-expert users. However, these interaction modalities require users to take full control of the moving robot, which requires the users to be familiar with robot teleoperation. As an alternative, we introduce \textsc{apple}, Adaptive Planner Parameter Learning from \emph{Evaluative Feedback} (real-time, scalar-valued assessments of behavior), which represents a less-demanding modality of interaction. Simulated and physical experiments show \textsc{apple} can achieve better performance compared to the planner with static default parameters and even yield improvement over learned parameters from richer interaction modalities.

• The paper demonstrates that APPLE improves mobile robot navigation by learning from simple evaluative feedback rather than expert-tuned parameters.
• APPLE utilizes a neural network to predict performance from user feedback, achieving competitive outcomes in both simulation and real-world tests.
• The approach effectively generalizes across diverse environments, reducing the need for manual parameter adjustments in autonomous navigation systems.

Essay: Adaptive Planner Parameter Learning from Evaluative Feedback

The paper "APPLE: Adaptive Planner Parameter Learning from Evaluative Feedback" introduces a novel method for improving mobile robot navigation systems by dynamically adjusting planner parameters based on evaluative feedback from users. This feedback-driven adaptation facilitates navigation in new and complex environments without the necessity for expert intervention or parameter tuning.

Mobile robot navigation has been traditionally addressed using classical approaches, which offer robust performance with verifiable safety guarantees. However, these systems often require manual adjustment of parameters when faced with new environments, which necessitates expert knowledge. Adaptive Planner Parameter Learning, or APPLE, is introduced as a method to circumvent this requirement by allowing robots to learn from simple feedback provided by non-expert users. Unlike earlier methods, such as teleoperated demonstrations or corrective interventions that demand a user take control of the robot, APPLE enables parameter learning solely from evaluative feedback like "good job" or "bad job."

The paper situates APPLE within the broader landscape of machine learning for navigation and relates it closely to the Adaptive Planner Parameter Learning (APPL) paradigm. The authors have enhanced APPL to allow learning from the less demanding modality of evaluative feedback, distinguishing APPLE from previous works that rely on similarity-based predictors for parameter selection. APPLE instead bases parameter adjustments on the expected performance as reflected in the feedback, showing competitive or improved results even when compared to richer interaction modalities.

Experiments were conducted in both simulated and real-world environments, with the simulated environments based on the Benchmarking Autonomous Robot Navigation (BARN) dataset. The findings highlight that APPLE, under both discrete and continuous parameter policy configurations, effectively outperformed default settings and APPL implementations in a meaningful fraction of test environments. Particularly noteworthy is APPLE's ability to generalize across varied scenarios, including unseen environments, demonstrating applicability beyond the specific conditions under which it was trained.

The method leverages a neural network for predicting evaluative feedback which is used to guide parameter selection. Special emphasis is placed on the ability of APPLE to utilize feedback provided by non-experts, widening its usability and application potential. This research offers practical implications: APPLE could be assimilated into navigation systems that are required to function autonomously across diverse settings without the need for frequent, expert-led system configuration.

The paper acknowledges APPLE's reliance on the quantity and quality of feedback, a factor which might limit its applicability if such feedback is sparse or inconsistent. Future research directions suggested by the authors include exploring ways to learn from discrete feedback in continuous parameter spaces, perhaps with augmented informational cues from users.

In essence, the work proposes a significant step forward in adaptive navigation, providing a means for autonomous systems to dynamically adjust to environmental challenges using user-friendly interaction modalities. APPLE's contribution is particularly pertinent in the context of deploying autonomous navigation systems in real-world environments where immediate expert intervention is neither feasible nor efficient.