# GROOVE: Geometry-Guided Reduction of Operational-Space Jerk in VLA Execution

arXiv: https://arxiv.org/abs/2609.13695 · HTML: https://mlsanity.com/paper/2609.13695/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Efficient VLA inference and adaptation, one of 13 directions in Vision-language-action models.
- Builds on 24 of its 28 references that are in the map.
- First posted on arXiv 12 Sep 2026.

## Placed in

- Vision-language-action models / [Efficient VLA inference and adaptation](https://mlsanity.com/vla/efficient-vla-inference-and-adaptation/)

## Builds on (most cited first, 24 of 24)

- [OpenVLA: An Open-Source Vision-Language-Action Model](https://mlsanity.com/paper/2406.09246/) (2024)
- [π0.5: a Vision-Language-Action Model with Open-World Generalization](https://mlsanity.com/paper/2504.16054/) (2025)
- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning](https://mlsanity.com/paper/2306.03310/) (2023)
- [GR00T N1: An Open Foundation Model for Generalist Humanoid Robots](https://mlsanity.com/paper/2503.14734/) (2025)
- [Real-Time Execution of Action Chunking Flow Policies](https://mlsanity.com/paper/2506.07339/) (2025)
- [OSQP: An Operator Splitting Solver for Quadratic Programs](https://mlsanity.com/paper/1711.08013/) (2017)
- [Bidirectional Decoding: Improving Action Chunking via Guided Test-Time Sampling](https://mlsanity.com/paper/2408.17355/) (2024)
- [Leave No Observation Behind: Real-time Correction for VLA Action Chunks](https://mlsanity.com/paper/2509.23224/) (2025)
- [Adaptive Action Chunking at Inference-time for Vision-Language-Action Models](https://mlsanity.com/paper/2604.04161/) (2026)
- [Learning Native Continuation for Action Chunking Flow Policies](https://mlsanity.com/paper/2602.12978/) (2026)
- [Jerk-limited Real-time Trajectory Generation with Arbitrary Target States](https://mlsanity.com/paper/2105.04830/) (2021)
- [Realtime-VLA V2: Learning to Run VLAs Fast, Smooth, and Accurate](https://mlsanity.com/paper/2603.26360/) (2026)
- [PACE: Phase-Aware Chunk Execution for Robot Policies with Action Chunking](https://mlsanity.com/paper/2606.00537/) (2026)
- [SEAM: Smooth Execution of Action-Chunked Motion for Vision-Language-Action Policies](https://mlsanity.com/paper/2607.04609/) (2026)
- [ChunkFlow: Towards Continuity-Consistent Chunked Policy Learning](https://mlsanity.com/paper/2607.12992/) (2026)
- [On the Performance of Jerk-Constrained Time-Optimal Trajectory Planning for Industrial Manipulators](https://mlsanity.com/paper/2404.07889/) (2024)
- [LiPo: A Lightweight Post-optimization Framework for Smoothing Action Chunks Generated by Learned Policies](https://mlsanity.com/paper/2506.05165/) (2025)
- [Start Right, Arrive Right: Asynchronous Execution via Initial Noise Selection](https://mlsanity.com/paper/2606.19774/) (2026)
- [SmoothVLA: Aligning Vision-Language-Action Models with Physical Constraints via Intrinsic Smoothness Optimization](https://mlsanity.com/paper/2603.13925/) (2026)
- [Learning High-Frequency Continuous Action Chunks in Latent Space](https://mlsanity.com/paper/2605.24931/) (2026)
- [From Inference Efficiency to Embodied Efficiency: Revisiting Efficiency Metrics for Vision-Language-Action Models](https://mlsanity.com/paper/2603.19131/) (2026)
- [Smoother Action Chunking Flow Policy via Prior-Corrected Orthogonal Trust-Region Guidance](https://mlsanity.com/paper/2605.24433/) (2026)
- [ACG: Action Coherence Guidance for Flow-based Vision-Language-Action models](https://mlsanity.com/paper/2510.22201/) (2025)

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

- [JitterVLA: A Replay Benchmark for Motion Quality in VLA-Driven Human-Robot Manipulation](https://mlsanity.com/paper/2610.13628/) (2026)

## Abstract (arXiv)

Chunked vision language action (VLA) policies execute several commands per query, but jerk within chunks and across replanning boundaries can induce oscillatory motion and sharp actuator transients. We present GROOVE, an online regulator that searches directional correction regions around the raw three dimensional end effector (EEF) path, without retraining or additional VLA inference. It optimizes the new chunk using delivered commands as boundary conditions, reducing boundary and within chunk jerk while bounding cumulative translation and local axis angle deviation from the raw plan after every command. Using quadratic programs (QPs), GROOVE generates a cube reference and thirteen directional candidates, then selects the one with the lowest command space jerk under a reference relative deviation cap. On a held out LIBERO benchmark, GROOVE achieves the largest reductions among the evaluated methods, reducing translational and rotational EEF jerk by 33.02% and 43.42%, respectively, with task success of 95.75% versus 93.75% for raw execution. Across 50 matched UR5e pairs with measured execution timing, it reduces translational and rotational tool center point (TCP) jerk by 16.39% and 19.49% and joint current slew by 29.09%.