# Risk-aware Integrated Task and Motion Planning for Versatile Snake Robots under Localization Failures

arXiv: https://arxiv.org/abs/2502.19690 · HTML: https://mlsanity.com/paper/2502.19690/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 45 references that are in the map.
- First posted on arXiv 27 Feb 2025.

## Builds on (most cited first, 5 of 5)

- [Monte Carlo Motion Planning for Robot Trajectory Optimization Under Uncertainty](https://mlsanity.com/paper/1504.08053/) (2015)
- [Non-Gaussian Risk Bounded Trajectory Optimization for Stochastic Nonlinear Systems in Uncertain Environments](https://mlsanity.com/paper/2203.03038/) (2022)
- [Bi-directional Value Learning for Risk-aware Planning Under Uncertainty: Extended Version](https://mlsanity.com/paper/1902.05698/) (2019)
- [Chance Constrained Motion Planning for High-Dimensional Robots](https://mlsanity.com/paper/1811.03073/) (2018)
- [Convex Risk Bounded Continuous-Time Trajectory Planning in Uncertain Nonconvex Environments](https://mlsanity.com/paper/2106.05489/) (2021)

## Abstract (arXiv)

Snake robots enable mobility through extreme terrains and confined environments in terrestrial and space applications. However, robust perception and localization for snake robots remain an open challenge due to the proximity of the sensor payload to the ground coupled with a limited field of view. To address this issue, we propose Blind-motion with Intermittently Scheduled Scans (BLISS) which combines proprioception-only mobility with intermittent scans to be resilient against both localization failures and collision risks. BLISS is formulated as an integrated Task and Motion Planning (TAMP) problem that leads to a Chance-Constrained Hybrid Partially Observable Markov Decision Process (CC-HPOMDP), known to be computationally intractable due to the curse of history. Our novelty lies in reformulating CC-HPOMDP as a tractable, convex Mixed Integer Linear Program. This allows us to solve BLISS-TAMP significantly faster and jointly derive optimal task-motion plans. Simulations and hardware experiments on the EELS snake robot show our method achieves over an order of magnitude computational improvement compared to state-of-the-art POMDP planners and > 50% better navigation time optimality versus classical two-stage planners.