The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes? (1710.05512v1)

Abstract: A successful grasp requires careful balancing of the contact forces. Deducing whether a particular grasp will be successful from indirect measurements, such as vision, is therefore quite challenging, and direct sensing of contacts through touch sensing provides an appealing avenue toward more successful and consistent robotic grasping. However, in order to fully evaluate the value of touch sensing for grasp outcome prediction, we must understand how touch sensing can influence outcome prediction accuracy when combined with other modalities. Doing so using conventional model-based techniques is exceptionally difficult. In this work, we investigate the question of whether touch sensing aids in predicting grasp outcomes within a multimodal sensing framework that combines vision and touch. To that end, we collected more than 9,000 grasping trials using a two-finger gripper equipped with GelSight high-resolution tactile sensors on each finger, and evaluated visuo-tactile deep neural network models to directly predict grasp outcomes from either modality individually, and from both modalities together. Our experimental results indicate that incorporating tactile readings substantially improve grasping performance.

• The paper demonstrates that integrating tactile sensing with visual data significantly improves grasp prediction outcomes compared to using vision alone.
• It employs over 9,000 grasp trials with GelSight high-resolution sensors and convolutional neural networks to train visuo-tactile models.
• The multimodal approach achieved a test accuracy of 77.8% and a 94% grasp success rate, highlighting its potential for advanced robotic manipulation.

Evaluation of Touch Sensing for Grasp Outcome Prediction in Robotic Systems

The paper "The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes?" examines the contribution of tactile sensing to the efficacy of robotic grasping. The paper focuses on the integration of tactile sensors with visual modalities within a multimodal sensing framework to enhance the accuracy of predicting grasp outcomes. This comprehensive research has a large dataset and sophisticated analytical methods, making it an essential contribution to the field of robotics and tactile sensing technology.

Research Methodology

The researchers collected data from over 9,000 grasping trials using a two-finger gripper equipped with GelSight high-resolution tactile sensors on each finger. These sensors capture detailed information about contact surfaces, allowing neural networks to deduce grasp success probabilities. The paper evaluates visuo-tactile deep neural network models based on either modality separately and in combination.

The training of these models was executed through a convolutional neural network architecture, leveraging pretrained models on ImageNet for initialization. The tactile data was not treated generically but was processed through the same neural architecture, thus maintaining consistency across modalities.

Key Findings

One of the pivotal results reported is that the inclusion of tactile readings improves the prediction accuracy by a considerable margin over visual data alone. Specifically, the dual-modality model achieved a test accuracy of 77.8%, surpassing models that rely solely on visual inputs or tactile features.

It was further demonstrated that tactile data, even when processed with one sensor, improved classification accuracy compared to vision-only approaches, emphasizing the value tactile input adds to grasp prediction models. The multimodal model showed successful grasp prediction rates of 94%, which is significantly higher than the baseline methods used for data collection and vision-only models.

Implications and Future Directions

The findings offer promising implications for developing more efficient robotic systems capable of handling real-world objects with diverse textures and properties. By combining visual and tactile data, robots can make better-informed decisions, contributing to advancements in autonomous robotic manipulation for complex tasks like object sorting or assembly lines.

Future research may explore optimizing the integration of visuo-tactile data. Exploring advanced proposing mechanisms for grasp locations could reduce the number of attempts required for successful outcomes, making robotic systems more agile and efficient. It's also essential to address scenarios post-liftoff, such as detecting slippage, which further integrates tactile feedback in real-time for maintaining secure grasps.

Conclusion

This paper successfully showcases the importance of tactile sensing in complex robotic manipulation tasks. Leveraging high-resolution tactile sensors such as GelSight, and combining them with visual perception, results in substantial improvement in predicting grasp outcomes. The results underline a vital step forward in robotic sensory fusion methodologies, potentially leading to versatile, robust robotic systems that excel in dynamic environments. This work will undoubtedly serve as a reference point for incorporating tactile sensing into broader robotics research and applications.