In robotic, task goals can be conveyed through various modalities, such as language, goal images, and goal videos. However, natural language can be ambiguous, while images or videos may offer overly detailed specifications. To tackle these challenges, we introduce CrayonRobo that leverages comprehensive multi-modal prompts that explicitly convey both low-level actions and high-level planning in a simple manner. Specifically, for each key-frame in the task sequence, our method allows for manual or automatic generation of simple and expressive 2D visual prompts overlaid on RGB images. These prompts represent the required task goals, such as the end-effector pose and the desired movement direction after contact. We develop a training strategy that enables the model to interpret these visual-language prompts and predict the corresponding contact poses and movement directions in SE(3) space. Furthermore, by sequentially executing all key-frame steps, the model can complete long-horizon tasks. This approach not only helps the model explicitly understand the task objectives but also enhances its robustness on unseen tasks by providing easily interpretable prompts. We evaluate our method in both simulated and real-world environments, demonstrating its robust manipulation capabilities.

Convolutional neural networks (CNNs) excel at learning spatial patterns through local receptive fields, while channel attention mechanisms enhance feature discrimination by emphasizing informative channels. Traditional approaches like SENet generate channel weights through global pooling and bottlenecked MLPs, but fundamentally ignore cross-channel dependencies—a critical limitation that discards contextual relationships between features. To address this, we propose the Cross-Channel Graph Attention mechanism (CCA), which explicitly models channel interactions via graph neural networks. Specifically, CCA represents channels as graph nodes, constructs sparse KNN graphs to capture inter-channel dependencies, and performs attention-based feature aggregation. Theoretically, we prove that CCA strictly expands the expressive power of conventional channel attention through full-rank attention matrices, while maintaining superior generalization via sparsity-induced Rademacher complexity bounds. Furthermore, we establish that CCA can universally approximate any continuous channel mixing function with controlled neighbor size (Theorem 3). Extensive experiments on image classification (CIFAR-10/100, mini-ImageNet) and super-resolution (Set5/14) validate that CCA-equipped networks achieve state-of-the-art performance—improving ResNet34 by 2.16% TOP-1 accuracy on CIFAR-10 and boosting VDSR’s PSNR by 0.43dB on ×3 scaling. Both theoretical and empirical analyses demonstrate that CCA achieves an optimal balance between feature interaction capacity and computational efficiency through its graph-structured attention paradigm. The code is available at https://github.com/openreview-pro/CCA

Recent advancements in 3D robotic manipulation have improved grasping of everyday objects, but transparent and specular materials remain challenging due to depth sensing limitations. While several 3D reconstruction and depth completion approaches address these challenges, they suffer from setup complexity or limited observation information utilization. To address this, leveraging the power of single-view 3D object reconstruction approaches, we propose a training-free framework SR3D that enables robotic grasping of transparent and specular objects from a single-view observation. Specifically, given single-view RGB and depth images, SR3D first uses the external visual models to generate 3D reconstructed object mesh based on RGB image. Then, the key idea is to determine the 3D object’s pose and scale to accurately localize the reconstructed object back into its original depth corrupted 3D scene. Therefore, we propose view matching and keypoint matching mechanisms, which leverage both the 2D and 3D’s inherent semantic and geometric information in the observation to determine the object’s 3D state within the scene, thereby reconstructing an accurate 3D depth map for effective grasp detection. Experiments in both simulation and real-world show the reconstruction effectiveness of SR3D. More demonstrations can be found at: https://sites.google.com/view/sr3dtech/

Robot manipulation relies on accurately predicting contact points and end-effector directions to ensure successful operation. However, learning-based robot manipulation, trained on a limited category within a simulator, often struggles to achieve generalizability, especially when confronted with extensive categories. Therefore, we introduce an innovative approach for robot manipulation that leverages the robust reasoning capabilities of Multimodal Large Language Models (MLLMs) to enhance the stability and generalization of manipulation. By fine-tuning the injected adapters, we preserve the inherent common sense and reasoning ability of the MLLMs while equipping them with the ability for manipulation. The fundamental insight lies in the introduced fine-tuning paradigm, encompassing object category understanding, affordance prior reasoning, and object-centric pose prediction to stimulate the reasoning ability of MLLM in manipulation. During inference, our approach utilizes an RGB image and text prompt to predict the end effector's pose in chain of thoughts. After the initial contact is established, an active impedance adaptation policy is introduced to plan the upcoming way-points in a closed-loop manner. Moreover, in real world, we design a test-time adaptation (TTA) strategy for manipulation to enable the model better adapt to the current real-world scene configuration. Experiments in simulator and real-world show the promising performance of Mani-pLLM. More details and demonstrations can be found at https://sites.google.com/view/manipllm.

In recent years, with the significant improvement of Gener-ative Adversarial Networks (GANs), fake images generated by GAN become hardly distinguishable from real ones, thus threatening the authentication of digital images. To resolve this issue, several fake image detectors based on supervised binary classification have been designed. However, current methods remain vulnerable when testing samples are gener-ated by an unknown GAN model. In this work, an unsuper-vised domain adaptation strategy is introduced to improve the performance in the generalization of GAN-generated image detection by using a small number of unlabeled images from the target domain. Self-Attention block and novel loss function have been constructed to optimize the domain adaptation process, thus getting a better generalization. Experimental results demonstrate that the proposed scheme achieves high detection accuracy with few unlabeled images in the target domain, which shows that unsupervised methods can be used for the detection of GAN-generated images.

Existing deepfake detection methods have achieved high accuracy under the intra-database scenario, but most of them suffer from a significant performance drop when evaluated on cross-database experiments. In this paper, we regard the deepfake detection task as domain generalization problem and design a Facial Decomposition-based Domain Generalization framework to learn a more generalized feature representation. To optimize our framework, we experimentally analysis that current CNN-based detectors tend to overfit the facial semantic content but neglect the common shared traces of deepfake, thus a facial semantic content decomposition based dual-branch network is designed to reduce the representation discrepancies among multiple source domains in a specific feature space to be domain invariant. Besides, based on the consideration that the distribution discrepancies are much larger among the fake faces than the real ones, a hybrid loss function is designed to enlarge the distance among samples from different categories even in cross-domain scene. Extensive experiments demonstrate that the proposed method can achieve better performance compared with state-of-the-art methods, especially on cross-database evaluation.

Polar codes have been selected as the channel coding scheme for 5G new radio physical downlink control channel (NR PDCCH). In 5G NR PDCCH, blind detection is performed by user equipment (UE) to obtain its intended information. During blind detection, false alarm (FA) may happen which means that the UE can decode successfully even there is no intended information. FA can be caused by many factors. In this paper, we focus on the FA caused by radio network temporary identifier (RNTI), we call it RNTI-FA. RNTI-FA may happen even in high SNR range and results in abnormal network status. We firstly investigate the causes of RNTI-FA. Then a novel RNTIFA mitigate scheme, based on the Hamming distance metric, is proposed to reduce the RNTI-FA. The simulation results show that the RNTI-FA rate can be reduced from $10^{-1}$ to $10^{-5}$ or lower with negligible performance loss in the medium-to-high SNR range.

3-D flash memory faces a number of challenges, including thermal issues and process variation. The high temperature will cause charge loss and lead to the fluctuation of threshold voltage. To address the thermal issue of 3-D flash memory, this paper presents ThermAlloc, a novel thermal-aware physical space allocation strategy for open-channel solid-state drive with 3-D charge trapping flash memory. ThermAlloc permutes the allocation of physical blocks. Consecutively accessed logical blocks are distributed to different physical locations in order to prevent the accumulation of hotspots. The objective is to postpone garbage collection operations and keep the distribution of block temperature well under control. We demonstrate the viability of the proposed technique using a set of extensive experiments. Experimental results show that ThermAlloc can reduce the peak temperature by 26.94% with 3.15% extra worst-case response time in comparison with the baseline scheme.

Three-dimensional (3D) flash memory is emerging as an attractive solution to overcome the scaling bottleneck in sub-20 nanometer design. Compared to the conventional planar flash memory, current 3D flash memory adopts charge-trap technology that can significantly enhance cell density and storage capacity. Despite these advantages, novel material and fabricate process in charge-trap flash memory bring new challenges. Recent studies demonstrate that charge-trap flash is sensitive to thermal yield. This issue does not happen in two-dimensional flash memory which adopts floating gate technology. For 3D flash memory with charge-trap technology, the high temperature will incur both charge loss and retention degradation. The large capacity data block in 3D flash memory also causes extra garbage collection overhead, which makes the temperature issue even worse. This paper presents TempLoad, a temperature-aware data allocation strategy for 3D charge-trap flash memory. TempLoad is a novel hardware and file system interface that can transparently allocate physical space based on the temperature status. TempLoad adopts several address mapping strategies to fully utilize the storage capacity and reduce the garbage collection overhead. The objective is to prevent the generation of hotspots and enhance the data integrity of 3D flash memory. Experimental results show that the proposed technique can reduce the peak temperature by 28.49% and reduce uncorrectable page errors by 83.71% with negligible timing overhead in comparison with previous work.

Deep convolutional neural networks (CNNs) are widely adopted in intelligent systems with unprecedented accuracy but at the cost of a substantial amount of data movement. Although recent development in processing-in-memory (PIM) architecture seeks to minimize data movement by computing the data at the dedicated nonvolatile device, how to jointly explore the computation capability of PIM and utilize the highly parallel property of neural network remains a critical issue.

Convolutional neural networks (CNNs) are widely adopted in artificial intelligent systems. In contrast to conventional computing centric applications, the computational and memory resources of CNN applications are mixed together in the network weights. This incurs a significant amount of data movement, especially for highdimensional convolutions. Although recent embedded 3D-stacked Processing-in-Memory (PIM) architecture alleviates this memory bottleneck to provide fast near-data processing, memory is still a limiting factor of the entire system. An unsolved key challenge is how to efficiently allocate convolutions to 3D-stacked PIM to combine the advantages of both neural and computational processing.

Three-dimensional (3D) flash memory stacks layers of data storage cells vertically to overcome the scaling limits in conventional planar NAND flash memory. Current 3D flash memory faces new challenges including thermal issues and complex manufacturing process. This paper presents TheraPhy, a novel thermal-aware physical space allocation strategy for three-dimensional flash memory storage systems. TheraPhy permutes the allocation of physical blocks. Consecutively accessed logical blocks are distributed to different physical locations in order to prevent the accumulation of hotspots. TheraPhy requires no changes to the file system, on-chip memory hierarchy, or hardware implementation of 3D flash memory. Based on TheraPhy, we present an address mapping strategy that is capable of determining the allocation of physical blocks based on their thermal status. We demonstrate the viability of the proposed technique using a set of extensive experiments. Experimental results show that TheraPhy can reduce the peak temperature by 15.39% with less than 1% extra erase overhead in comparison with the baseline scheme.