LRF-Net: Learning Local Reference Frames for 3D Local Shape Description and Matching

The local reference frame (LRF) acts as a critical role in 3D local shape description and matching. However, most of existing LRFs are hand-crafted and suffer from limited repeatability and robustness. This paper presents the first attempt to learn an LRF via a Siamese network that needs weak supervision only. In particular, we argue that each neighboring point in the local surface gives a unique contribution to LRF construction and measure such contributions via learned weights. Extensive analysis and comparative experiments on three public datasets addressing different application scenarios have demonstrated that LRF-Net is more repeatable and robust than several state-of-the-art LRF methods (LRF-Net is only trained on one dataset). In addition, LRF-Net can significantly boost the local shape description and 6-DoF pose estimation performance when matching 3D point clouds.Comment: 28 pages, 14 figure

Learn from Incomplete Tactile Data: Tactile Representation Learning with Masked Autoencoders

The missing signal caused by the objects being occluded or an unstable sensor is a common challenge during data collection. Such missing signals will adversely affect the results obtained from the data, and this issue is observed more frequently in robotic tactile perception. In tactile perception, due to the limited working space and the dynamic environment, the contact between the tactile sensor and the object is frequently insufficient and unstable, which causes the partial loss of signals, thus leading to incomplete tactile data. The tactile data will therefore contain fewer tactile cues with low information density. In this paper, we propose a tactile representation learning method, named TacMAE, based on Masked Autoencoder to address the problem of incomplete tactile data in tactile perception. In our framework, a portion of the tactile image is masked out to simulate the missing contact region. By reconstructing the missing signals in the tactile image, the trained model can achieve a high-level understanding of surface geometry and tactile properties from limited tactile cues. The experimental results of tactile texture recognition show that our proposed TacMAE can achieve a high recognition accuracy of 71.4% in the zero-shot transfer and 85.8% after fine-tuning, which are 15.2% and 8.2% higher than the results without using masked modeling. The extensive experiments on YCB objects demonstrate the knowledge transferability of our proposed method and the potential to improve efficiency in tactile exploration.Comment: This paper is accepted at IROS 202

Effect of bias voltage on the tribological and sealing properties of rubber seals modified by DLC films

Diamond-like carbon (DLC) films were deposited on nitrile butadiene rubber (NBR) by the DC magnetron sputtering under different bias voltages. Raman spectra revealed that the variation of bias voltage could tune the carbon bond structure in DLC films. Both the hardness and Young's modulus increased with the increasing bias voltage. Tribological tests revealed that the DLC-coated NBR prepared at the bias voltage of -200 V exhibited low wear rate due to its high hardness. The sealing property was studied by evaluating the leakage rate of volatile liquid in a simple apparatus. All DLC films resulted in less leakage rate as compared to the raw rubber under large stress. The lowest leakage rate occurred in the DLC-coated NBR prepared with a bias voltage of -200 V, which was associated with the theoretical calculations (Persson's theory). It was attributed to the synergetic effects of the variations of the Young's modulus and root-mean-square (Rms) roughness. The low Young's modulus and Rms, controlled by regulating bias voltage, could enhance actual contact area and reduce the leakage rate