Draw the boundary where uncertainty is high
For autonomous robots, the safety boundary should be drawn where the system's uncertainty about the semantic map is high. A 2024 paper introduced a navigation method (COSMAu-Nav) that uses Gaussian processes to model terrain traversability, occupancy, and topography from 3D semantic point clouds, explicitly providing uncertainty estimates [3]. This allows the robot to plan paths that avoid high-uncertainty regions, effectively making the safety boundary dynamic—it expands in areas where the map is uncertain and contracts where the map is confident. The paper demonstrated this in simulated and real-world unstructured environments, showing that uncertainty-aware planning is feasible for real-time navigation.
This approach contrasts with fixed safety margins (e.g., always keep 1 meter from obstacles). Instead, the boundary is a function of the model's confidence. In practice, this means if a robot's semantic memory is uncertain about a terrain type (e.g., is it solid ground or rubble?), the safety boundary should be drawn farther away. The 2024 paper's use of Gaussian processes makes this uncertainty explicit and computationally tractable, which is key for embedded systems.
Safety boundaries for novel or unseen objects
When a robot must navigate to objects it has never seen before (zero-shot navigation), the safety boundary must account for the system's generalization ability. A 2023 study on language-driven zero-shot object navigation (L-ZSON) found that a simple framework using CLIP (a vision-language model) and classical exploration—with no additional training—matched the navigation efficiency of a state-of-the-art method trained for 500 million steps on indoor data [4]. This suggests that for novel objects, the safety boundary can be relatively permissive if the system uses robust open-vocabulary representations. However, the same study found that these baselines often struggled to leverage language descriptions, so the boundary should be tighter when the target is described by attributes (e.g., 'red chair') rather than just its name.
Another 2023 study proposed a semantic similarity network (SSNet) for zero-shot object navigation, using cosine similarity between word embeddings to generalize to novel classes [5]. In AI2-THOR simulations, SSNet outperformed baseline models, indicating that safety boundaries can be extended to novel objects if the system uses semantic similarity rather than fixed class labels. The catch: these results are from simulated environments, so real-world safety boundaries may need to be more conservative until the approach is validated in physical settings.
About These Sources
This answer is built on 5 peer-reviewed studies — published from 2022 to 2024, 1 from 2024 or later, 2 in Q1 journals, collectively cited 263 times — selected as the most relevant from 5 studies that passed quality screening, drawn from 35 papers retrieved from a database of over 500 million.
Sources used in this answer
Sleep facilitates spatial memory but not navigation using the Minecraft Memory and Navigation task
In a Minecraft-based navigation task with 29 participants, spatial memory accuracy declined after wakefulness but was maintained after sleep, while navigation improved over both delays, indicating separate safety margins for memory and navigation.
Spatial navigation and memory: A review of the similarities and differences relevant to brain models and age
A 2023 review argues that navigation is a dynamically acquired skill and memory is internally driven, with overlapping but distinct neural networks, supporting the idea that safety boundaries should differ for memory recall versus navigation actions.
Continuous Online Semantic Implicit Representation for Autonomous Ground Robot Navigation in Unstructured Environments
COSMAu-Nav uses Gaussian processes to model terrain traversability and occupancy with uncertainty, enabling safe path planning in unstructured environments; it was tested in simulation and on a real robot.
CoWs on Pasture: Baselines and Benchmarks for Language-Driven Zero-Shot Object Navigation
In a benchmark of 22 baselines across 90k navigation episodes, a simple CLIP-based approach with classical exploration matched the efficiency of a model trained for 500M steps, but struggled with attribute-based language descriptions.
Zero-Shot Object Goal Visual Navigation
SSNet, using semantic word embeddings and cosine similarity, outperformed baselines in zero-shot object navigation in AI2-THOR simulations, showing that semantic similarity can generalize to novel object classes.