Autonomous robots are increasingly deployed in complex, uncertain environments. How can we enable robots to effectively manage uncertainty while pursuing diverse tasks? Can we design a framework that allows robots to dynamically adjust their behavior based on task-specific uncertainty requirements, improving performance and generalization across various scenarios?

Generalized Uncertainty Integration for Decision-Making and Execution (GUIDE)

Robots performing navigation tasks in complex environments face significant challenges due to uncertainty in state estimation arising from sensor noise, environmental changes, and incomplete information. Effectively managing this uncertainty is crucial, but the optimal approach varies depending on the specific details of the task: different tasks may require varying levels of precision in different regions of the environment. For instance, a robot navigating a crowded space might need precise localization near obstacles but can operate effectively with less precise state estimates in open areas. This varying need for certainty in different parts of the environment, depending on the task, calls for navigation policies that can adapt their uncertainty management strategies based on task-specific requirements. In this paper, we present a framework for integrating task-specific uncertainty requirements directly into navigation policies. We introduce the concept of a Task-Specific Uncertainty Map (TSUM), which represents acceptable levels of state estimation uncertainty across different regions of the operating environment for a given task. Using TSUM, we propose Generalized Uncertainty Integration for Decision-Making and Execution (GUIDE), a policy conditioning framework that incorporates these uncertainty requirements into the robot's decision-making process. We find that conditioning policies on TSUMs not only provides an effective way to express task-specific uncertainty requirements but also enables the robot to reason about the context-dependent value of certainty and adapt its behavior accordingly. We show how integrating GUIDE into reinforcement learning frameworks allows the agent to learn navigation policies that effectively balance task completion and uncertainty management without the need for explicit reward engineering. We evaluate GUIDE at scale on a variety of real-world robotic navigation tasks and find that it demonstrates significant improvements in task completion rates compared to baseline methods that do not explicitly consider task-specific uncertainty.

Task Description:

Start at the dock and navigate around the central fountain, then navigate around the left fountain, and finally around the right fountain.

Teaser: GUIDE's performance in response to a given task. The off-white line shows the ASV's trajectory. Task-Specific Uncertainty Maps (TSUMs) are displayed above each subtask area. Red areas indicate exact localization usage, while other areas used belief-based localization, demonstrating GUIDE's adaptive uncertainty management.

Experimental Videos

These videos demonstrate GUIDE's performance on various tasks, both familiar and novel. Each task, specified in natural language, is processed by GUIDE to generate Task-Specific Uncertainty Maps (TSUMs) that inform decision-making. The videos feature Autonomous Surface Vehicles (ASVs) operating at 13x real-time speed. Red areas in the trajectory map indicate the use of exact localization, which offers higher precision at a greater cost. Other areas utilize belief-based localization. These demonstrations highlight GUIDE's adaptive behavior, showcasing its ability to balance task performance with efficient uncertainty management across different scenarios.

Note: the coordinates mentioned in the videos are dummy values to maintain anonymity.

Task 1:

Start at the dock and navigate around the central fountain, then navigate around the left fountain, and finally around the right fountain.

Task 2:

Start at [80,70] and reach the dock while avoiding the left half of the environment.

Task 3:

Start and end at the dock. Go around the perimeter of the area and visit coordinate [60,70].

Task 4:

Go to point [80,90] and then go around the central fountain and return to the dock.

Task 5:

Go to point [80,60] and then go to point [80, 20] and then go to [80, 70] and finally return to the dock. This task has to be completed by avoiding the right half of the area

Task 6:

Navigate the perimeter of the area. Then proceed to the central fountain and circumnavigate it. After that, move towards the left fountain and navigate around it. Finally, return to the dock.

Some comments on the behavior of the ASV:

We observed distinct behaviors in GUIDEd agents across various tasks. Analyzing the specific task shown in the figure below, GUIDEd agents strategically adjust their reliance on precise position estimation versus noisier estimates. In areas where the TSUMs indicate high precision is necessary --- such as navigating near obstacles or close to the perimeters (highlighted in yellow) --- the agents opt for exact positioning despite the higher operational cost. Conversely, in less critical regions (shown in purple), they rely on less precise, noisy estimates. This adaptability allows GUIDEd agents to manage uncertainty more efficiently than baselines, resulting in faster completion times and smoother trajectories. Although not perfect --- occasionally missing sections of the perimeter (indicated by black shaded regions) --- GUIDEd agents significantly outperform baselines like SAC-P with engineered rewards.

Authors

Anonymous Authors

Citation


              @article{anonymous2025guide,

                author = {Anonymous},

                title = {Enhancing Robot Navigation Policies with Task-Specific Uncertainty Management},

                journal = {},

                year = {},

              }