FAVLA: A Force-Adaptive Fast-Slow VLA model for Contact-Rich Robotic Manipulation

TL;DR

FAVLA introduces a force-adaptive, dual-frequency VLA model that improves contact-rich robotic manipulation by decoupling perception and control, enabling faster reactive responses to forces and impacts.

Contribution

The paper presents FAVLA, a novel force-adaptive framework that separates slow perception from fast contact-aware control, with a force adapter and adaptive scheduling for improved reactivity.

Findings

FAVLA outperforms baselines in contact-rich tasks.

Achieves higher success rates and reactivity.

Effective with smaller contact forces.

Abstract

Force/torque feedback can substantially improve Vision-Language-Action (VLA) models on contact-rich manipulation, but most existing approaches fuse all modalities at a single operating frequency. This design ignores the mismatched sampling rates of real robot sensors, forcing downsampling of the high-frequency contact cues needed for reactive correction. Combined with common VLM-action-expert (AE) pipelines that execute action chunks largely open loop between expensive VLM updates, unified-frequency fusion often yields delayed responses to impacts, stick-slip, and force spikes. We propose FAVLA, a force-adaptive fast-slow VLA that decouples slow perception planning from fast contact-aware control. FAVLA runs a slow VLM at a fixed low frequency to encode modalities to produce latent representations and to predict near-future force variation. A fast AE then executes at a variable high frequency, conditioning on the latest force sequence data to generate reactive actions. We further introduce a force adapter that injects high-frequency force features into multiple AE layers, and adaptively schedules the AE's execution frequency based on the VLM's predicted force variation. Extensive experiments on contact-rich tasks demonstrate that FAVLA significantly outperforms baselines, achieving superior reactivity and success rates, especially with a smaller contact force during manipulation.