REVIEW 8 cited by
TLA: Tactile-Language-Action Model for Contact-Rich Manipulation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Significant progress has been made in vision-language models. However, language-conditioned robotic manipulation for contact-rich tasks remains underexplored, particularly in terms of tactile sensing. To address this gap, we introduce the Tactile-Language-Action (TLA) model, which effectively processes sequential tactile feedback via cross-modal language grounding to enable robust policy generation in contact-intensive scenarios. In addition, we construct a comprehensive dataset that contains 24k pairs of tactile action instruction data, customized for fingertip peg-in-hole assembly, providing essential resources for TLA training and evaluation. Our results show that TLA significantly outperforms traditional imitation learning methods (e.g., diffusion policy) in terms of effective action generation and action accuracy, while demonstrating strong generalization capabilities by achieving over 85\% success rate on previously unseen assembly clearances and peg shapes. We publicly release all data and code in the hope of advancing research in language-conditioned tactile manipulation skill learning. Project website: https://sites.google.com/view/tactile-language-action/
Forward citations
Cited by 8 Pith papers
-
Feeling the Unexpected: ResTacVLA for Contact-Rich Manipulation via Residual Tactile Representation
Residual tactile representations plus surprise-aware gating let VLA policies master contact-rich robot tasks that vision-only models fail.
-
$N_0$-TWAM: Scaling Tactile-Native World-Action Model for Contact-Rich Manipulation
A scaled tactile-native world-action model jointly predicts vision, touch, and action and outperforms vision-only baselines on contact-rich sim and real robot tasks.
-
Representation-Aligned Tactile Grounding for Contact-Rich Robotic Manipulation
Future tactile prediction applied to intermediate action-expert features, rather than visual-language or final-action features, improves contact-rich manipulation in SmolVLA and π0.
-
Tactile Modality Fusion for Vision-Language-Action Models
A FiLM-based tactile fusion method that conditions VLA visual features on frozen pretrained touch embeddings improves real-robot insertion success, speed, and force control relative to vision-only and concatenation baselines.
-
Force-Aware Residual DAgger via Trajectory Editing for Precision Insertion with Impedance Control
TER-DAgger uses force-prediction mismatches to trigger human corrections and residual-policy training, lifting precision-insertion success from 40.0% to 77.2% on average.
-
ViTacWorld: Scaling Visuo-Tactile World Models for Contact-Rich Robot Manipulation
An action-conditioned visuo-tactile world model generates synthetic camera-plus-touch rollouts that, mixed with real demonstrations, improve downstream contact-rich manipulation policies.
-
VLA-Touch: Enhancing Vision-Language-Action Models with Dual-Level Tactile Feedback
Tactile feedback, provided both as language descriptions for planning and as force signals for action refinement, improves vision-language-action robot policies on contact-rich manipulation tasks.
-
Tactile-VLA: Unlocking Vision-Language-Action Model's Physical Knowledge for Tactile Generalization
Tactile-VLA fuses tactile sensing into a vision-language-action model so force-related instructions and corrective reasoning transfer to new contact-rich tasks with few demonstrations.
Discussion (0). Sign in to comment.