VTLA: Vision-Tactile-Language-Action Model with Preference Learning for Insertion Manipulation (2505.09577v1)

Abstract: While vision-LLMs have advanced significantly, their application in language-conditioned robotic manipulation is still underexplored, especially for contact-rich tasks that extend beyond visually dominant pick-and-place scenarios. To bridge this gap, we introduce Vision-Tactile-Language-Action model, a novel framework that enables robust policy generation in contact-intensive scenarios by effectively integrating visual and tactile inputs through cross-modal language grounding. A low-cost, multi-modal dataset has been constructed in a simulation environment, containing vision-tactile-action-instruction pairs specifically designed for the fingertip insertion task. Furthermore, we introduce Direct Preference Optimization (DPO) to offer regression-like supervision for the VTLA model, effectively bridging the gap between classification-based next token prediction loss and continuous robotic tasks. Experimental results show that the VTLA model outperforms traditional imitation learning methods (e.g., diffusion policies) and existing multi-modal baselines (TLA/VLA), achieving over 90% success rates on unseen peg shapes. Finally, we conduct real-world peg-in-hole experiments to demonstrate the exceptional Sim2Real performance of the proposed VTLA model. For supplementary videos and results, please visit our project website: https://sites.google.com/view/vtla

An Analysis of "VTLA: Vision-Tactile-Language-Action Model with Preference Learning for Insertion Manipulation"

This paper introduces the VTLA framework, which aims to enhance robotic manipulation through an innovative integration of vision, tactile, and language modalities in contact-rich scenarios. The VTLA system is grounded in a simulation environment that provides domain-randomized vision-tactile-action-instruction pairs. It advances beyond traditional models by incorporating comprehensive sensory inputs to refine action prediction in robotic insertion tasks. This paper leverages Direct Preference Optimization (DPO) to mitigate overfitting and enhance model generalization.

Technical Contributions

The VTLA framework's contributions are centered around two primary innovations:

1. Vision-Guided Temporally Enhanced Tokens (VGTE): VGTE tokens emphasize visual cues and integrate temporal fusion just before tokenization. This design ameliorates the temporal reasoning limitations inherent in Vision-LLMs (VLMs), effectively augmenting VTLA's cross-modal temporal comprehension capabilities.
2. Direct Preference Optimization (DPO): Through DPO, the VTLA model receives regression-like supervision, promoting richer training signals and advancing generalization to unseen environments and tasks.

Experimental Evaluations and Results

The VTLA framework was assessed against traditional imitation learning methods and existing models including TLA and VLA. The evaluation metrics were focused on Goal Convergence Rate (GCR) and the L1 distance between predicted and actual actions.

• Simulation and Real-World Experiments: VTLA displayed significant prowess with a success rate exceeding 90% in peg-in-hole tasks, outperforming conventional models that utilize singular sensory inputs. Moreover, the framework demonstrated robust transferability from simulation to real-world scenarios, maintaining a high success rate (95%) even with diminishing assembly clearances.
• Ablation Studies: The paper's ablation paper highlights DPO's contribution to generalization with a notable 16% improvement in goal convergence rates on out-of-distribution data, reinforcing the necessity of preference learning.

Implications and Future Directions

This research provides critical insights into tactile-embedded VLA frameworks, suggesting that VTLA's multimodal approach can lead to enhanced robotic manipulation capabilities, particularly in contact-intensive environments. The confluence of vision, tactile sensing, and language processing opens avenues for more human-like perceptual-motor reasoning, elevating prospects for AI-driven automation in complex settings.

The paper identifies areas for further exploration, notably in refining tactile-language alignment and enhancing visual-tactile integration. Given the potential of LLMs in robotic domains, subsequent research could explore optimizing tactile representations independent of visual encoders. Additionally, the application of advanced domain randomization methods could further bridge Sim2Real gaps, rendering robotic systems more robust across variable physical contexts.

In conclusion, "VTLA: Vision-Tactile-Language-Action Model with Preference Learning for Insertion Manipulation" represents a pivotal move towards more advanced, multimodal robotic systems, paving the way for profound developments in AI-driven manipulation tasks. The proposed framework holds promise for wider adoption in industrial and research applications, where complex contact conditions must be navigated with precision and adaptability.