RCSP: Risk-Sensitive Conjectural Scenario Planning for Safe Dynamic Robot Navigation
RCSP is a predictive planning layer that addresses the near-miss commitment problem in mobile robot navigation by evaluating candidate commands against plausible short-horizon obstacle futures. Simulations show it enhances safety and path quality but adds latency, revealing its role as a complementary module for existing navigation stacks.
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Key points
- RCSP tackles the predictive near-miss commitment problem where a safe velocity may lead to a blocked passage.
- It maintains a lightweight belief over motion conjectures, samples future interactions, and penalizes high-risk tails.
- In MuJoCo and ROS2/Gazebo tests, RCSP reduces dynamic near-miss failures at the cost of increased latency.
- On DynaBARN/Jackal benchmarks, tuned DWA and TEB still outperform RCSP in strict success rates.
Why it matters
This matters because RCSP tackles the predictive near-miss commitment problem where a safe velocity may lead to a blocked passage.
Technical impact
May affect compliance requirements, model release timing, data governance, and enterprise procurement.
[2605.26348] RCSP: Risk-Sensitive Conjectural Scenario Planning for Safe Dynamic Robot Navigation
[Submitted on 25 May 2026]
Title:RCSP: Risk-Sensitive Conjectural Scenario Planning for Safe Dynamic Robot Navigation
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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.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2605.26348 [cs.RO]
(or arXiv:2605.26348v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.26348
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zhengye Han [view email] [v1] Mon, 25 May 2026 21:47:36 UTC (2,902 KB)
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