One Filter to Deploy Them All: Robust Safety for Quadrupedal Navigation in Unknown Environments (2412.09989v1)

Abstract: As learning-based methods for legged robots rapidly grow in popularity, it is important that we can provide safety assurances efficiently across different controllers and environments. Existing works either rely on a priori knowledge of the environment and safety constraints to ensure system safety or provide assurances for a specific locomotion policy. To address these limitations, we propose an observation-conditioned reachability-based (OCR) safety-filter framework. Our key idea is to use an OCR value network (OCR-VN) that predicts the optimal control-theoretic safety value function for new failure regions and dynamic uncertainty during deployment time. Specifically, the OCR-VN facilitates rapid safety adaptation through two key components: a LiDAR-based input that allows the dynamic construction of safe regions in light of new obstacles and a disturbance estimation module that accounts for dynamics uncertainty in the wild. The predicted safety value function is used to construct an adaptive safety filter that overrides the nominal quadruped controller when necessary to maintain safety. Through simulation studies and hardware experiments on a Unitree Go1 quadruped, we demonstrate that the proposed framework can automatically safeguard a wide range of hierarchical quadruped controllers, adapts to novel environments, and is robust to unmodeled dynamics without a priori access to the controllers or environments - hence, "One Filter to Deploy Them All". The experiment videos can be found on the project website.

• The paper introduces an OCR safety-filter framework that provides a generalizable safety controller for quadrupedal robots in unpredictable environments.
• It leverages LiDAR-based inputs and disturbance estimation to dynamically adjust safety parameters while minimally intervening with nominal policies.
• Experimental results demonstrate reduced collision rates and successful zero-shot safety across various controllers and challenging terrains.

Essay on the OCR Safety-Filter Framework for Quadrupedal Navigation

The research paper presents an innovative framework for enhancing the safety and reliability of quadrupedal robots navigating unknown environments. As the application of legged robotics expands into complex real-world scenarios — from hazardous site inspections to search and rescue missions — ensuring the robots' operability under uncertain conditions becomes crucial. This paper addresses the limitations of existing safety mechanisms for quadrupeds by introducing an Observation-Conditioned Reachability (OCR) safety-filter framework that leverages learning-based techniques to augment the adaptability and robustness of these robots.

Problem Statement and Existing Solutions

The core challenge addressed in this paper is the development of a generalizable safety controller that can operate across diverse unknown environments and various quadrupedal policies without the necessity of prior policy or environmental knowledge. Prevailing approaches, such as model-based methods, although providing theoretical safety guarantees, struggle with computational demands and robustness issues due to model mismatches. On the other hand, RL-based controllers unduly prioritize agility, potentially compromising safety, especially in unseen, cluttered settings.

Recent methods utilizing safety critics and backup policies have made strides toward safer RL-based locomotion. However, they often remain bound by the dynamics and environmental distributions from their training phases, limiting their real-world applicability. The OCR framework circumvents these confines by leveraging Hamilton-Jacobi reachability analysis to precompute optimal control-theoretic safety solutions, integrating them seamlessly with real-time adaptations from environmental observations and disturbance estimation.

Technical Advancements and Methodology

The framework distinctively utilizes an OCR Value Network (OCR-VN) that adapts a pre-learned safety value function in response to dynamic environmental changes and system uncertainties. The OCR-VN efficiently constructs an adaptive safety filter in real-time through two pivotal components:

1. LiDAR-Based Input: This facilitates real-time environment perception, allowing the OCR-VN to dynamically adjust the safety regions based on the latest obstacle configurations encountered by the robot.
2. Disturbance Estimation Module: The module assesses dynamics uncertainties by analyzing recent state-action histories, enabling the system to anticipate and adapt to dynamics variances actively.

The integration of these components into the safety framework not only furnishes robust safety assurances but also retains the performance of an underlying nominal policy by minimally intervening — calibrated to step in only when safety is compromised.

Experimental Evaluation and Results

The paper rigorously tests the framework within diverse simulation environments and hardware settings using a Unitree Go1 quadruped. The results showcase the OCR framework's ability to dynamically adapt to unforeseen obstacles and maintain operational safety across varying terrains and disturbance conditions. Notably, the framework successfully operates alongside different hierarchical controllers, whether model-based or learning-based, demonstrating its broad applicability.

Numerically, the OCR framework outperforms existing methods on several metrics, highlighting its efficacy. For instance, the reduction of collision rates across various navigation policies and environmental configurations underscores its robustness. Moreover, the framework achieves zero-shot safety across multiple controllers, reinforcing its potential to universally safeguard legged robots in real-world scenarios.

Implications and Future Directions

The OCR framework delineates a significant step forward in robust quadrupedal robot navigation, promising to enhance the deployment of legged robots in unpredictable field environments. By eschewing policy- and environment-specific predefined safety maps, this research advances a more flexible and practical approach to robot safety.

Looking forward, extensions of this work could involve the integration of alternative sensory inputs or further refinements in dynamics estimation to address failures at higher sophistication levels. Additionally, optimizing the computational efficiency during real-time applications could widen the framework's accessibility and implementation across various robotic contexts.

In conclusion, this paper delivers a robust, adaptable safety mechanism for quadrupedal robots, paving the way for their future deployment in complex, uncontrolled environments, and enhancing the field of autonomous robot navigation. This research marks a pivotal yet natural progression in the journey towards safe and intelligent mobile robots.