Enhancing Generalizable 6D Pose Tracking of an In-Hand Object with Tactile Sensing
arXiv:2210.04026 · doi:10.1109/LRA.2023.3337690
Abstract
When manipulating an object to accomplish complex tasks, humans rely on both vision and touch to keep track of the object's 6D pose. However, most existing object pose tracking systems in robotics rely exclusively on visual signals, which hinder a robot's ability to manipulate objects effectively. To address this limitation, we introduce TEG-Track, a tactile-enhanced 6D pose tracking system that can track previously unseen objects held in hand. From consecutive tactile signals, TEG-Track optimizes object velocities from marker flows when slippage does not occur, or regresses velocities using a slippage estimation network when slippage is detected. The estimated object velocities are integrated into a geometric-kinematic optimization scheme to enhance existing visual pose trackers. To evaluate our method and to facilitate future research, we construct a real-world dataset for visual-tactile in-hand object pose tracking. Experimental results demonstrate that TEG-Track consistently enhances state-of-the-art generalizable 6D pose trackers in synthetic and real-world scenarios. Our code and dataset are available at https://github.com/leolyliu/TEG-Track.
References in corpus (6)
- DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation
- Using Collocated Vision and Tactile Sensors for Visual Servoing and Localization
- Active Visuo-Haptic Object Shape Completion
- PIZZA: A Powerful Image-only Zero-Shot Zero-CAD Approach to 6 DoF Tracking
- Efficient Visuo-Haptic Object Shape Completion for Robot Manipulation
- TacGNN:Learning Tactile-based In-hand Manipulation with a Blind Robot