It is difficult to guarantee an empty living environment to prevent unexpected contact between the object being manipulated by the robot and unplanned obstacles. In this paper, we propose a planar compliant contact control method for planar manipulation to cope with unexpected contact. We first use sheet gel as a multi-dimensional passive elastic element and combine it with a two-finger gripper to design a multi-dimensional elastic gripper. Subsequently, we explore the lumped parameter model for the force-displacement relationship of gel deformation and combine the model with the high impedance motion of robots to design an elastic interaction controller. The controller not only actively adjusts the deformation of the gel to provide the desired contact force and torque depending on contact, but also performs avoidance by following the surface of obstacles. Finally, we design and deploy several planar compliant contact experiments to validate the proposed method and demonstrate the unexpected contact response in human-robot co-packing. The results show that our method enables the robot to remain compliant in the face of unexpected contact caused by unplanned obstacles, which provides a guarantee for safe manipulation. Physics experiments can be viewed in the attached video.

This paper presents a learning-based method for transparent surface estimation from a single view polarization image. Existing shape from polarization(SfP) methods have the difficulty in estimating transparent shape since the inherent transmission interference heavily reduces the reliability of physics-based prior. To address this challenge, we propose the concept of physics-based prior confidence, which is inspired by the characteristic that the transmission component in the polarization image has more noise than reflection. The confidence is used to determine the contribution of the interfered physics-based prior. Then, we build a network(TransSfP) with multi-branch architecture to avoid the destruction of relationships between different hierarchical inputs. To train and test our method, we construct a dataset for transparent shape from polarization with paired polarization images and ground-truth normal maps. Extensive experiments and comparisons demonstrate the superior accuracy of our method. Our cdataset and code are publicly available at https://github.com/shaomq2187/TransSfP

In this paper, we propose a novel real-time 6D object pose estimation framework based on Point-wise Attention Keypoints Voting Network (PAV-Net). Compared with previous methods that use all features indiscriminately, we evaluate and integrate the visible points features before estimation to deal with the unstructured and uneven properties of point-wise features. Specifically, we first locate the object roughly by object detection and transfer the captured point cloud coordinates to the local center. Then we extract point-wise features from RGB images and point clouds respectively and perform semantic segmentation. Finally, the point-wise features are screened and integrated with the help of the attention keypoints voting to predict the accurate keypoint coordinates, and the 6D object pose can be obtained within keypoints fitting. The proposed method can effectively avoid external interference and improve the efficiency of influential point features utilization by point-wise attention voting so that the framework only needs a simple feature extraction network support to have better real-time performance. Extensive experiments confirm this conclusion and show that the performance of proposed framework on LineMOD and YCB-Video datasets is superior to other real-time pose estimation methods at the same speed.