# BiC-MPPI: Goal-Pursuing, Sampling-Based Bidirectional Rollout Clustering Path Integral for Trajectory Optimization

arXiv: https://arxiv.org/abs/2410.06493 · HTML: https://mlsanity.com/paper/2410.06493/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 19 references that are in the map.
- First posted on arXiv 9 Oct 2024.

## Builds on (most cited first, 9 of 9)

- [Information Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving](https://mlsanity.com/paper/1707.02342/) (2017)
- [Benchmarking Metric Ground Navigation](https://mlsanity.com/paper/2008.13315/) (2020)
- [A survey on trajectory clustering analysis](https://mlsanity.com/paper/1802.06971/) (2018)
- [Autonomous Navigation of AGVs in Unknown Cluttered Environments: log-MPPI Control Strategy](https://mlsanity.com/paper/2203.16599/) (2022)
- [Biased-MPPI: Informing Sampling-Based Model Predictive Control by Fusing Ancillary Controllers](https://mlsanity.com/paper/2401.09241/) (2024)
- [Recent Advances in Path Integral Control for Trajectory Optimization: An Overview in Theoretical and Algorithmic Perspectives](https://mlsanity.com/paper/2309.12566/) (2023)
- [Path Integral Control with Rollout Clustering and Dynamic Obstacles](https://mlsanity.com/paper/2403.18066/) (2024)
- [Bidirectional Sampling Based Search Without Two Point Boundary Value Solution](https://mlsanity.com/paper/2010.14692/) (2020)
- [MPPI-IPDDP: Hybrid Method of Collision-Free Smooth Trajectory Generation for Autonomous Robots](https://mlsanity.com/paper/2208.02439/) (2022)

## Built on by (most cited first, 1 of 1)

- [GeNIE: A Generalizable Navigation System for In-the-Wild Environments](https://mlsanity.com/paper/2506.17960/) (2025)

## Abstract (arXiv)

This paper introduces the Bidirectional Clustered MPPI (BiC-MPPI) algorithm, a novel trajectory optimization method aimed at enhancing goal-directed guidance within the Model Predictive Path Integral (MPPI) framework. BiC-MPPI incorporates bidirectional dynamics approximations and a new guide cost mechanism, improving both trajectory planning and goal-reaching performance. By leveraging forward and backward rollouts, the bidirectional approach ensures effective trajectory connections between initial and terminal states, while the guide cost helps discover dynamically feasible paths. Experimental results demonstrate that BiC-MPPI outperforms existing MPPI variants in both 2D and 3D environments, achieving higher success rates and competitive computation times across 900 simulations on a modified BARN dataset for autonomous navigation. GitHub: https://github.com/i-ASL/BiC-MPPI