# Decremental Dynamics Planning for Robot Navigation

arXiv: https://arxiv.org/abs/2503.20521 · HTML: https://mlsanity.com/paper/2503.20521/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2025.

## Built on by (most cited first, 2 of 2)

- [DREAM: Domain-aware Reasoning for Efficient Autonomous Underwater Monitoring](https://mlsanity.com/paper/2509.13666/) (2025)
- [APPLV: Adaptive Planner Parameter Learning from Vision-Language-Action Model](https://mlsanity.com/paper/2603.08862/) (2026)

## Abstract (arXiv)

Most, if not all, robot navigation systems employ a decomposed planning framework that includes global and local planning. To trade-off onboard computation and plan quality, current systems have to limit all robot dynamics considerations only within the local planner, while leveraging an extremely simplified robot representation (e.g., a point-mass holonomic model without dynamics) in the global level. However, such an artificial decomposition based on either full or zero consideration of robot dynamics can lead to gaps between the two levels, e.g., a global path based on a holonomic point-mass model may not be realizable by a non-holonomic robot, especially in highly constrained obstacle environments. Motivated by such a limitation, we propose a novel paradigm, Decremental Dynamics Planning that integrates dynamic constraints into the entire planning process, with a focus on high-fidelity dynamics modeling at the beginning and a gradual fidelity reduction as the planning progresses. To validate the effectiveness of this paradigm, we augment three different planners with DDP and show overall improved planning performance. We also develop a new DDP-based navigation system, which achieves first place in the simulation phase of the 2025 BARN Challenge. Both simulated and physical experiments validate DDP's hypothesized benefits.