RCSP: Risk-Sensitive Conjectural Scenario Planning for Safe Dynamic Robot Navigation

• URL: http://arxiv.org/abs/2605.26348v1
• Date: Mon, 25 May 2026 21:47:36 GMT
• Title: RCSP: Risk-Sensitive Conjectural Scenario Planning for Safe Dynamic Robot Navigation
• Abstract summary: Risk-Sensitive Conjectural Scenario Planning (RCSP) is a planning layer that evaluates candidate commands against plausible short-horizon obstacle futures.<n>In controlled MuJoCo bottleneck tasks, RCSP planner reaches the goal without collisions and yields higher secondary safety and path-quality point estimates.
• Score: 16.960126375377133
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Mobile robots can fail before they collide: a velocity that is safe now may commit the robot to a passage that moving obstacles will soon close. We study this predictive near-miss commitment problem and propose Risk-Sensitive Conjectural Scenario Planning (RCSP), a planning layer that evaluates candidate commands against plausible short-horizon obstacle futures. RCSP maintains a lightweight belief over local motion conjectures, samples future interactions, penalizes high-risk tails, and executes through a local safety check. In controlled MuJoCo bottleneck tasks, the RCSP planner reaches the goal without collisions and yields higher secondary safety and path-quality point estimates than a non-adaptive predictor, with additional latency. In ROS2/Gazebo, adding the local safety layer to a standard Nav2 stack reduces dynamic near-miss failures. On official DynaBARN/Jackal transfer, tuned DWA and TEB remain stronger on strict benchmark success, revealing the boundary of the approach. These simulation results position RCSP as a predictive-risk module that complements existing navigation stacks in dynamic bottleneck regimes.

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