TAP-VLA: Tactile Annotation Prompting for Vision Language Action Models

• URL: http://arxiv.org/abs/2606.29089v1
• Date: Sat, 27 Jun 2026 21:06:06 GMT
• Title: TAP-VLA: Tactile Annotation Prompting for Vision Language Action Models
• Abstract summary: We propose a simple framework that supplies tactile feedback through visual augmentation rather than architectural change.<n>Because the architecture is untouched, the approach requires no tactile pre-training, adds negligible compute, and stays close to the pre-training distribution.<n>Across four contact-rich tasks, TAP-VLA succeeds on 78% of trials, compared to under 50% for vision-only fine-tuning and alternative tactile-fusion baselines.
• Score: 12.700838638661047
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language-Action (VLA) models demonstrate impressive reasoning over visual, semantic, and spatial task variations by leveraging large-scale vision and language pre-training. They remain, however, largely blind to contact forces, which seldom manifest clearly in visual feedback but are central to contact-rich manipulation. Tactile sensing measures these forces directly, but integrating it into VLAs is difficult: tactile data is absent from the large-scale corpora used to pre-train VLAs, so adding it as a new input modality induces a distribution shift that erodes the very pre-training that makes VLAs effective. We propose Tactile Annotation Prompting for Vision-Language-Action models (TAP-VLA), a simple framework that supplies tactile feedback through visual augmentation rather than architectural change. TAP-VLA extracts shear fields from visuo-tactile sensors and overlays them as spatially-grounded vectors onto the multi-view RGB images the policy already consumes, yielding a clear, interpretable tactile cue in the VLA's native observation space. Because the architecture is untouched, the approach requires no tactile pre-training, adds negligible compute, and stays close to the pre-training distribution. Across four contact-rich tasks, TAP-VLA succeeds on 78% of trials, compared to under 50% for vision-only fine-tuning and alternative tactile-fusion baselines -- including tasks where the baselines perform no better than chance.

Related papers

• τ: Learning Touch-Augmented Vision-Language-Action Models from Future Visual Supervision [43.426795127841075]
  We present, a touch-augmented Vision-Language-Action (VLA) framework that learns an action-conditioned tactile representation from future visual supervision.<n>We also introduce TacAura, a dataset of synchronized vision, proprioception, and vision-based tactile signals across four representative contact-rich manipulation tasks.
  arXiv  Detail & Related papers  (2026-07-27T14:25:15Z)
• TACO: TActile World Model as a Self-COrrector forScalable VLA Post-Training [79.94818813802682]
  We introduce TACO, a tactile-aware world-model-driven framework for scalable VLA post-training in contact-rich manipulation.<n>To incorporate tactile corrective supervision into VLA post-training, TACO combines knowledge-insulated tactile adaptation with advantage-conditioned training.
  arXiv  Detail & Related papers  (2026-07-03T00:23:30Z)
• Two Bridges, One Pathway: From VLMs to Generalizable VLAs with Embodied Trajectory-Coupled Data [93.7685703383343]
  Vision-language models (VLMs) are powerful general-purpose reasoners, yet converting them into robot control policies is surprisingly difficult.<n>We argue that this gap can be bridged gradually with the right intermediate data.<n>We show that mixing task-relevant out-of-distribution ETC data with a small amount of action data enables the model to generalize to novel visual-language conditions.
  arXiv  Detail & Related papers  (2026-06-07T08:57:51Z)
• QuoVLA: Quotient Space for Vision-Language-Action Models [51.02329790939691]
  Vision-Language-Action (VLA) models commonly adapt pretrained VLM latents to robot control by mapping visual observations and language instructions to continuous actions.<n>Our textitQuotient Theory for VLA shows that pretrained VLM latents are not action-insufficient but action-sufficient.<n>We propose QuoVLA, a quotient-space framework for VLA that compresses pretrained VLM latents into action-sufficient representations.
  arXiv  Detail & Related papers  (2026-05-24T06:28:53Z)
• 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)
• TacVLA: Contact-Aware Tactile Fusion for Robust Vision-Language-Action Manipulation [27.000763540977506]
  Vision-Language-Action (VLA) models have demonstrated significant advantages in robotic manipulation.<n>We propose TacVLA, a fine-tuned VLA model by incorporating tactile modalities into the transformer-based policy.<n>We introduce a contact-aware gating mechanism that selectively activates tactile tokens only when contact is detected.
  arXiv  Detail & Related papers  (2026-03-13T05:20:41Z)
• TaF-VLA: Tactile-Force Alignment in Vision-Language-Action Models for Force-aware Manipulation [14.094740703476903]
  We introduce TaF-VLA, a framework that grounds high-dimensional tactile observations in physical interaction forces.<n>TaF-VLA policy significantly outperforms state-of-the-art tactile-vision-aligned and vision-only baselines on contact-rich tasks.
  arXiv  Detail & Related papers  (2026-01-28T07:34:41Z)
• Mantis: A Versatile Vision-Language-Action Model with Disentangled Visual Foresight [49.882469110319086]
  This paper introduces Mantis, a novel framework featuring a Disentangled Visual Foresight (DVF)<n>Mantis decouples visual prediction from the backbone with the combination of meta queries and a diffusion Transformer (DiT) head.<n>Mantis achieves a 96.7% success rate on LIBERO benchmark after fine-tuning.
  arXiv  Detail & Related papers  (2025-11-20T09:30:23Z)
• 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)
• ForceVLA: Enhancing VLA Models with a Force-aware MoE for Contact-rich Manipulation [62.58034332427291]
  ForceVLA is a novel end-to-end manipulation framework.<n>It treats external force sensing as a first-class modality within VLA systems.
  arXiv  Detail & Related papers  (2025-05-28T09:24:25Z)
• CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models [89.44024245194315]
  We introduce a method that incorporates explicit visual chain-of-thought (CoT) reasoning into vision-language-action models (VLAs)<n>We introduce CoT-VLA, a state-of-the-art 7B VLA that can understand and generate visual and action tokens.<n>Our experimental results demonstrate that CoT-VLA achieves strong performance, outperforming the state-of-the-art VLA model by 17% in real-world manipulation tasks and 6% in simulation benchmarks.
  arXiv  Detail & Related papers  (2025-03-27T22:23:04Z)

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.