ViTaR: Visuo-Tactile Residual Adaptation for Foundation VLA Manipulation
- URL: http://arxiv.org/abs/2608.15816v1
- Date: Sun, 16 Aug 2026 15:46:11 GMT
- Title: ViTaR: Visuo-Tactile Residual Adaptation for Foundation VLA Manipulation
- Abstract summary: We introduce ViTaR, which reframes tactile feedback from an action-generating perceptual input to an execution modulator.<n>On the UniVTAC benchmark spanning seven contact-rich tasks, ViTaR achieves 61.3% average success, a 30.6 percentage-point improvement over its frozen VLA base.
- Score: 4.374415463111511
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: As Vision-Language-Action (VLA) models scale toward real-world deployment, contact-rich manipulation exposes a critical blind spot: these policies encode broad visual-semantic priors yet remain unaware of local contact events, producing identical actions whether contact is established, lost, or destabilized. Existing remedies either modify VLA internals, risking catastrophic forgetting, or demand online reinforcement under near-failure contact conditions. Both grant tactile unbounded influence over action generation, conflicting with the priors that make VLAs generalizable. We introduce ViTaR, which reframes tactile feedback from an action-generating perceptual input to an execution modulator that selects and scales bounded residual corrections atop a frozen VLA, preserving pretrained capabilities by construction. ViTaR decomposes adaptation into two stages: Effect-Guided Modeling determines whether and which correction is locally justified via outcome-grounded preference evidence, and Residual Action Modulation converts this evidence into a residual choice with continuously scaled gain from real-time visuotactile observations. On the UniVTAC benchmark spanning seven contact-rich tasks, ViTaR achieves 61.3% average success, a 30.6 percentage-point improvement over its frozen VLA base that also surpasses purpose-built tactile baselines. Physical-robot experiments confirm that bounded tactile modulation transfers to real sensor noise and dynamics.
Related papers
- Harness VLA: Steering Frozen VLAs into Reliable Manipulation Primitives via Memory-Guided Agents [23.94713093511847]
We present Harness VLA, a memory-augmented agentic framework that exposes a frozen VLA as a retryable contact-rich primitive.<n>The framework learns the operating range of these fixed primitives from task-specific execution traces, global success rules, and failure models.<n>Across tabletop, household kitchen, and clean-to-randomized bimanual manipulation, Harness VLA improves over the strongest relevant baselines by 38.6 and 25.4 percentage points.
arXiv Detail & Related papers (2026-07-09T13:08:54Z) - 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) - VT-WAM: Visual-Tactile World Action Model for Contact-Rich Manipulation [22.0330577788623]
Contact-rich manipulation requires policies to react to local deformation, pressure, slip, and friction.<n>Existing visual-tactile policies usually feed tactile observations directly into action prediction.<n>We introduce VT-WAM, a Visual-Tactile World Action Model that jointly learns future visual prediction, tactile deformation prediction, and action prediction.
arXiv Detail & Related papers (2026-07-02T17:58:36Z) - Partial Skeleton Visibility for Action Recognition: A Constrained Field-of-View Approach [60.596944437968524]
PartialVisGraph is a novel framework for robust skeleton-based action recognition under constrained field-of-view.<n>It consistently achieves state-of-the-art accuracy under partial visibility, with gains of up to 68.8% on subsets with severe FoV restrictions.<n>Our approach offers a principled and practical pathway toward deployable skeleton-based action understanding in unconstrained environments.
arXiv Detail & Related papers (2026-07-01T10:03:11Z) - 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) - ReViP: Reducing False Completion in Vision-Language-Action Models with Vision-Proprioception Rebalance [50.05984919728878]
We present ReViP, a novel VLA framework with Vision-Proprioception Rebalance to enhance visual grounding and robustness under perturbations.<n>Specifically, we use an external VLM as a task-stage observer to extract real-time task-centric visual cues from visual observations.<n>To evaluate false completion, we propose the first False-Completion Benchmark Suite built on LIBERO with controlled settings such as Object-Drop.
arXiv Detail & Related papers (2026-01-23T11:31:07Z) - CompliantVLA-adaptor: VLM-Guided Variable Impedance Action for Safe Contact-Rich Manipulation [35.15898666328117]
We propose a CompliantVLA-adaptor that augments the state-of-the-art Vision-Language-Action (VLA) models with vision-language model (VLM)-informed context-aware variable impedance control (VIC)<n>Existing VLA systems (e.g., RDT, Pi0.5, OpenVLA-oft) typically output position, but lack force-aware adaptation, leading to unsafe or failed interactions in physical tasks involving contact, compliance, or uncertainty.<n>We demonstrate that our method outperforms the VLA baselines on a suite of complex contact-rich tasks, both in
arXiv Detail & Related papers (2026-01-21T23:52:40Z) - Counterfactual VLA: Self-Reflective Vision-Language-Action Model with Adaptive Reasoning [71.19675094463834]
This work introduces Counterfactual VLA, a self-reflective VLA framework that enables the model to reason about and revise its planned actions before execution.<n>CF-VLA first generates time-segmented meta-actions that summarize driving intent, and then performs counterfactual reasoning conditioned on both the meta-actions and the visual context.<n> Experiments on large-scale driving datasets show that CF-VLA improves trajectory accuracy by up to 17.6%, enhances safety metrics by 20.5%, and exhibits adaptive thinking.
arXiv Detail & Related papers (2025-12-30T19:04:17Z) - Generalized Face Forgery Detection via Adaptive Learning for Pre-trained Vision Transformer [54.32283739486781]
We present a textbfForgery-aware textbfAdaptive textbfVision textbfTransformer (FA-ViT) under the adaptive learning paradigm.
FA-ViT achieves 93.83% and 78.32% AUC scores on Celeb-DF and DFDC datasets in the cross-dataset evaluation.
arXiv Detail & Related papers (2023-09-20T06:51:11Z)
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.