Learning Adaptive Safety Margins for Visual Navigation
2026-07-20 • Robotics
RoboticsArtificial Intelligence
AI summaryⓘ
The authors address a common problem in indoor robot navigation where fixed safety margins either make robots take long detours or risky shortcuts. They create a safety critic that learns how much space (clearance) a robot should keep from obstacles, balancing safety and efficiency by using three factors: safety penalties, smoothness and detour penalties, and distance constraints. Their system is trained in simulation and then applied to real perception data, allowing robots to pick the best path among many options generated by a diffusion planner. Testing showed their method works better than alternatives and even transfers well to a real robot without extra tuning.
robot navigationsafety margindiffusion-based planningclearancecontrol barrier functionESDF (Euclidean Signed Distance Field)PointGoal navigationteacher-student trainingsimulation-to-real transfer
Authors
Junyi Hu, Shuaihang Yuan, Geeta Chandra Raju Bethala, Anthony Tzes, Yi Fang
Abstract
Robots in cluttered indoor spaces often fail not because they cannot generate collision-free paths, but because a fixed safety margin is mis-calibrated: conservative margins cause detours and timeouts, while permissive margins lead to near-boundary shortcuts under perception bias. Diffusion-based planners propose diverse trajectory candidates from egocentric RGB-D, yet reliable selection remains the bottleneck. We propose a context-conditioned safety critic that learns an adaptive clearance preference for ranking diffusion proposals, decomposed into three complementary terms: (i) a safety term with a clearance-budget penalty and a control-barrier-function residual for waypoint- and transition-wise safety, (ii) an efficiency term combining a smoothness penalty with a safety-gated detour-ratio penalty that avoids detours without incentivizing risky shortcuts, and (iii) a distance-constraint matching term that anchors the learned budget to realized ESDF clearances to prevent margin collapse. We train the critic with privileged ESDF geometry in simulation and distill it into a perception-only selector via a two-stage teacher-student procedure. On PointGoal navigation in HM3D and MP3D, including cross-dataset transfer, our method achieves the highest success rate (SR) and success weighted by path length (SPL) among strong diffusion, optimization, and RL baselines. Trained purely in simulation, it transfers to a Unitree G1 humanoid and navigates cluttered indoor scenes without task-specific tuning.