Mobile manipulation typically entails the base for mobility, the arm for accurate manipulation, and the camera for perception. The principle of Distant Mobility, Close Grasping(DMCG) is essential for holistic control. We propose Embod-ied Holistic Control for Mobile Manipulation(EHC-MM) with the embodied function of sig($\omega$): By formulating the DMCG principle as a Quadratic Programming (QP) problem, sig($\omega$) dynamically balances the robot's emphasis between movement and manipulation with the consideration of the robot's state and environment. In addition, we propose the Monitor-Position-Based Servoing (MPBS) with sig($\omega$), enabling the tracking of the target during the operation. This approach enables coordinated control among the robot's base, arm, and camera, enhancing task efficiency. Through extensive simulations and real-world experiments, our approach significantly improves both the success rate and efficiency of mobile manipulation tasks, achieving a 95.6% success rate in real-world scenarios and a 52.8% increase in time efficiency.

In this paper, we tackle the problem of grasping transparent and specular objects. This issue holds importance, yet it remains unsolved within the field of robotics due to failure of recover their accurate geometry by depth cameras. For the first time, we propose ASGrasp, a 6-DoF grasp detection network that uses an RGB-D active stereo camera. ASGrasp utilizes a two-layer learning-based stereo network for the purpose of transparent object reconstruction, enabling material-agnostic object grasping in cluttered environments. In contrast to existing RGB-D based grasp detection methods, which heavily depend on depth restoration networks and the quality of depth maps generated by depth cameras, our system distinguishes itself by its ability to directly utilize raw IR and RGB images for transparent object geometry reconstruction. We create an extensive synthetic dataset through domain randomization, which is based on GraspNet-1Billion. Our experiments demonstrate that ASGrasp can achieve over 90% success rate for generalizable transparent object grasping in both simulation and the real via seamless sim-to-real transfer. Our method significantly outperforms SOTA networks and even surpasses the performance upper bound set by perfect visible point cloud inputs. Project page: https://pku-epic.github.io/ASGrasp

Recent advances in vision transformers (ViTs) have achieved outstanding performance in visual recognition tasks, including image classification and detection. ViTs can learn global representations with their self-attention mechanism, but they are usually heavy-weight and unsuitable for resource-constrained devices. In this paper, we propose a novel linear feature attention (LFA) module to reduce computation costs for vision transformers and combine efficient mobile CNN modules to form a parameter-efficient and high-performance CNN-ViT hybrid model, called LightFormer, which can serve as a general-purpose backbone to learn both global and local representation. Comprehensive experiments demonstrate that LightFormer achieves competitive performance across different visual recognition tasks. On the ImageNet-1K dataset, LightFormer achieves top-1 accuracy of 78.5% with 5.5 million parameters. Our model also performs well when transferred to object detection and semantic segmentation tasks. On the MS COCO dataset, LightFormer attains mAP of 33.2 within the YOLOv3 framework, and on the Cityscapes dataset, with only a simple all-MLP decoder, LightFormer achieves mIoU of 78.5 and FPS of 15.3, surpassing state-of-the-art lightweight segmentation networks.

Deep neural networks executing with low precision at inference time can gain acceleration and compression advantages over their high-precision counterparts, but need to overcome the challenge of accuracy degeneration as the bit-width decreases. This work focuses on under 4-bit quantization that has a significant accuracy degeneration. We start with ternarization, a balance between efficiency and accuracy that quantizes both weights and activations into ternary values. We find that the hard threshold$\Delta $introduced in previous ternary networks for determining quantization intervals and the suboptimal solution of$\Delta $limit the performance of the ternary model. To alleviate it, we present Soft Threshold Ternary Networks (STTN), which enables the model to automatically determine ternarized values instead of depending on a hard threshold. Based on it, we further generalize the idea of soft threshold from ternarization to arbitrary bit-width, named Soft Threshold Quantized Networks (STQN). We observe that previous quantization relies on the rounding-to-nearest function, constraining the quantization solution space and leading to a significant accuracy degradation, especially in low-bit ($\leq3$-bits) quantization. Instead of relying on the traditional rounding-to-nearest function, STQN is able to determine quantization intervals by itself adaptively. Accuracy experiments on image classification, object detection and instance segmentation, as well as efficiency experiments on field-programmable gate array (FPGA) demonstrate that the proposed framework can achieve a prominent tradeoff between accuracy and efficiency. Code is available at:https://github.com/WeixiangXu/STTN.