AT-VLA: Adaptive Tactile Injection for Enhanced Feedback Reaction in Vision-Language-Action Models

• URL: http://arxiv.org/abs/2605.07308v1
• Date: Fri, 08 May 2026 06:17:08 GMT
• Title: AT-VLA: Adaptive Tactile Injection for Enhanced Feedback Reaction in Vision-Language-Action Models
• Abstract summary: Vision-Language-Action (VLA) models have significantly advanced the capabilities of robotic agents in executing diverse tasks.<n>Recent studies have attempted to incorporate tactile signals during downstream tasks, enabling pretrained VLAs to interpret tactile feedback.<n>We propose Adaptive Tactile Vision-Language-Action (AT-VLA), which introduces a novel Adaptive Tactile Injection mechanism.
• Score: 11.558382158969822
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-Language-Action (VLA) models have significantly advanced the capabilities of robotic agents in executing diverse tasks; however, they still face challenges in contact-rich manipulation scenarios that require precise physical interactions. To address this limitation, recent studies have attempted to incorporate tactile signals during downstream tasks, enabling pretrained VLAs to interpret tactile feedback. Nevertheless, introducing new modalities during finetuning, which are rarely present in the pretrain stage, may disrupt the pretrained capabilities of VLAs. In addition, the inherently slow inference speed of VLAs hampers real-time responsiveness and limits the effective utilization of tactile feedback for action adjustment. To overcome these challenges, we propose Adaptive Tactile Vision-Language-Action (AT-VLA), which introduces a novel Adaptive Tactile Injection mechanism. This mechanism dynamically determines the appropriate timing and locations for tactile injection, incorporating only when it significantly contributes to action generation, thereby minimizing interference with pretrained representations. Furthermore, to enable rapid and accurate tactile responses, we propose a Tactile Reaction Dual-Stream mechanism, which decouples sensory processing into a slow visual-language stream for low-frequency perceptual reasoning and a fast tactile control stream for high-frequency physical interaction understanding, achieving real-time close-loop responses within 0.04 s. Real-world experiments thoroughly validate the effectiveness of AT-VLA in contact-rich manipulation tasks. The project page is available at: https://sites.google.com/view/at-vla.

Related papers

• CR-VLA-Force: Learning Control-aware Compliance VLA Model for Robust Contact-rich Robotic Manipulation [22.8919569384701]
  We propose a Control-aware Compliance VLA framework for reactive control.<n>The CC-VLA model employs a multimodal mixture-of-experts (MoE) to encode force signal sequences and vision-language fused feature.<n>We show that CC-VLA significantly improves success rates in challenging force-perception tasks and enhances force-control precision.
  arXiv  Detail & Related papers  (2026-09-05T02:54:51Z)
• UniTacVLA: Unified Tactile Understanding and Prediction in Vision Language Action Models [32.87879913150572]
  We propose a unified tactile learning framework for contact-rich manipulation.<n>We show that our method improves success rate, manipulation accuracy, and contact robustness over existing methods.
  arXiv  Detail & Related papers  (2026-06-30T14:24:00Z)
• T-Rex: Tactile-Reactive Dexterous Manipulation [139.71263755530654]
  We introduce a variable-rate Mixture-of-Transformers (MoT) architecture equipped with a novel temporal tactile VQ-VAE encoder.<n>We demonstrate the effectiveness of tactile-reactive policies on 12 manipulation tasks requiring delicate force control and deformable object manipulation.
  arXiv  Detail & Related papers  (2026-06-15T17:59:55Z)
• Tube Diffusion Policy: Reactive Visual-Tactile Policy Learning for Contact-rich Manipulation [11.359539466233137]
  Tube Diffusion Policy (TDP) is a novel visual-tactile policy learning framework that bridges imitation learning with tube-based feedback control.<n>TDP learns an observation-conditioned feedback flow around nominal action chunks, forming an action tube that enables fast and adaptive reactions during execution.
  arXiv  Detail & Related papers  (2026-04-26T08:48:26Z)
• Modular Sensory Stream for Integrating Physical Feedback in Vision-Language-Action Models [48.57114740339529]
  MoSS is a modular sensory stream framework that adapts Vision-Language-Action models to leverage multiple sensory signals for action prediction.<n>To enable stable incorporation of new modalities, we adopt a two-stage training scheme that freezes pretrained VLA parameters in the early stage.<n>To better capture contact interaction dynamics, we incorporate an auxiliary task that predicts future physical signals.
  arXiv  Detail & Related papers  (2026-04-25T12:28:47Z)
• VTAM: Video-Tactile-Action Models for Complex Physical Interaction Beyond VLAs [47.982092015932444]
  Video-Action Models (VAMs) have emerged as a promising framework for embodied intelligence.<n>We introduce the Video-Tactile Action Model (VTAM), a multimodal world modeling framework that incorporates tactile perception as a grounding signal.<n>VTAM augments a pretrained video transformer with tactile streams via a lightweight modality transfer finetuning.
  arXiv  Detail & Related papers  (2026-03-24T17:45:06Z)
• HapticVLA: Contact-Rich Manipulation via Vision-Language-Action Model without Inference-Time Tactile Sensing [1.5861606459586157]
  We argue that tactile-aware manipulation can be learned offline and deployed without direct haptic feedback at inference.<n>We present HapticVLA, which proceeds in two tightly coupled stages: Safety-Aware Reward-Weighted Flow Matching (SA-RWFM) and Tactile Distillation (TD)
  arXiv  Detail & Related papers  (2026-03-16T13:24:58Z)
• FAVLA: A Force-Adaptive Fast-Slow VLA model for Contact-Rich Robotic Manipulation [20.067295745725257]
  Force/torque feedback can substantially improve Vision-Language-Action (VLA) models on contact-rich manipulation.<n>Most existing approaches fuse all modalities at a single operating frequency.<n>We propose FAVLA, a force-adaptive fast-slow VLA that decouples slow perception planning from fast contact-aware control.
  arXiv  Detail & Related papers  (2026-02-27T03:33:10Z)
• Robotic VLA Benefits from Joint Learning with Motion Image Diffusion [114.60268819583017]
  Vision-Language-Action (VLA) models have achieved remarkable progress in robotic manipulation by mapping multimodal observations and instructions directly to actions.<n>We propose joint learning with motion image diffusion, a novel strategy that enhances VLA models with motion reasoning capabilities.<n> Experiments in both simulation and real-world environments demonstrate that joint learning with motion image diffusion improves the success rate of pi-series VLAs to 97.5%.
  arXiv  Detail & Related papers  (2025-12-19T19:07:53Z)
• VLA-Touch: Enhancing Vision-Language-Action Models with Dual-Level Tactile Feedback [21.08021535027628]
  We present VLA-Touch, an approach that enhances generalist robot policies with tactile sensing.<n>Our method introduces two key innovations: (1) a pipeline that leverages a pretrained tactile-language model that provides semantic tactile feedback for high-level task planning, and (2) a diffusion-based controller that refines VLA-generated actions with tactile signals for contact-rich manipulation.
  arXiv  Detail & Related papers  (2025-07-23T07:54:10Z)
• SP-VLA: A Joint Model Scheduling and Token Pruning Approach for VLA Model Acceleration [70.72227437717467]
  Vision-Language-Action (VLA) models have attracted increasing attention for their strong control capabilities.<n>Their high computational cost and low execution frequency hinder their suitability for real-time tasks such as robotic manipulation and autonomous navigation.<n>We propose SP-VLA, a unified framework that accelerates VLA models by jointly scheduling models and pruning tokens.
  arXiv  Detail & Related papers  (2025-06-15T05:04:17Z)
• Reactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for Contact-Rich Manipulation [58.95799126311524]
  Humans can accomplish contact-rich tasks using vision and touch, with highly reactive capabilities such as fast response to external changes and adaptive control of contact forces.<n>Existing visual imitation learning approaches rely on action chunking to model complex behaviors.<n>We introduce TactAR, a low-cost teleoperation system that provides real-time tactile feedback through Augmented Reality.
  arXiv  Detail & Related papers  (2025-03-04T18:58:21Z)
• Learning Compliance Adaptation in Contact-Rich Manipulation [81.40695846555955]
  We propose a novel approach for learning predictive models of force profiles required for contact-rich tasks. The approach combines an anomaly detection based on Bidirectional Gated Recurrent Units (Bi-GRU) and an adaptive force/impedance controller.
  arXiv  Detail & Related papers  (2020-05-01T05:23:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.