REVIEW 6 cited by
SAFER-Splat: A Control Barrier Function for Safe Navigation with Online Gaussian Splatting Maps
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
SAFER-Splat (Simultaneous Action Filtering and Environment Reconstruction) is a real-time, scalable, and minimally invasive action filter, based on control barrier functions, for safe robotic navigation in a detailed map constructed at runtime using Gaussian Splatting (GSplat). We propose a novel Control Barrier Function (CBF) that not only induces safety with respect to all Gaussian primitives in the scene, but when synthesized into a controller, is capable of processing hundreds of thousands of Gaussians while maintaining a minimal memory footprint and operating at 15 Hz during online Splat training. Of the total compute time, a small fraction of it consumes GPU resources, enabling uninterrupted training. The safety layer is minimally invasive, correcting robot actions only when they are unsafe. To showcase the safety filter, we also introduce SplatBridge, an open-source software package built with ROS for real-time GSplat mapping for robots. We demonstrate the safety and robustness of our pipeline first in simulation, where our method is 20-50x faster, safer, and less conservative than competing methods based on neural radiance fields. Further, we demonstrate simultaneous GSplat mapping and safety filtering on a drone hardware platform using only on-board perception. We verify that under teleoperation a human pilot cannot invoke a collision. Our videos and codebase can be found at https://chengine.github.io/safer-splat.
Forward citations
Cited by 6 Pith papers
-
SplatCtrl: Perception-Action Coupling via Gaussian Scene Representations and Reactive Robot Control
SplatCtrl couples real-time isotropic Gaussian scene reconstruction from RGB-D with continuous GPDF-derived SDFs inside control-barrier QP-IK for collision-free 6-DoF robot motion in dynamic environments.
-
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses
The survey organizes over 400 papers on embodied AI safety into a multi-level taxonomy and flags overlooked issues such as fragile multimodal fusion and unstable planning under jailbreaks.
-
ReMoSPLAT: Reactive Mobile Manipulation Control on a Gaussian Splat
ReMoSPLAT achieves reactive mobile-manipulation collision avoidance by querying distances from a Gaussian Splat reconstruction, matching a ground-truth-SDF controller in simulation.
-
VISTA: Open-Vocabulary, Task-Relevant Robot Exploration with Online Semantic Gaussian Splatting
VISTA couples a view-diversity information metric with CLIP semantics in a receding-horizon planner to improve open-vocabulary object search during online Gaussian Splatting mapping on robots.
-
Navigating Polytopes with Safety: A Control Barrier Function Approach
A closed-form control barrier function keeps polytope agents inside polytope safe sets by checking all agent vertices against all boundary half-spaces.
-
HAMMER: Heterogeneous, Multi-Robot Semantic Gaussian Splatting
A multi-robot system aligns heterogeneous camera streams into a common frame and continually trains a semantic 3D Gaussian Splatting map for language-guided navigation.
Discussion (0). Continue with ORCID to comment.