Coarse-to-Fine Learning for Multi-Pipette Localisation in Robot-Assisted In Vivo Patch-Clamp

In vivo image-guided multi-pipette patch-clamp is essential for studying cellular interactions and network dynamics in neuroscience. However, current procedures mainly rely on manual expertise, which limits accessibility and scalability. Robotic automation presents a promising solution, but achieving precise real-time detection of multiple pipettes remains a challenge. Existing methods focus on ex vivo experiments or single pipette use, making them inadequate for in vivo multi-pipette scenarios. To address these challenges, we propose a heatmap-augmented coarse-to-fine learning technique to facilitate multi-pipette real-time localisation for robot-assisted in vivo patch-clamp. More specifically, we introduce a Generative Adversarial Network (GAN)-based module to remove background noise and enhance pipette visibility. We then introduce a two-stage Transformer model that starts with predicting the coarse heatmap of the pipette tips, followed by the fine-grained coordination regression module for precise tip localisation. To ensure robust training, we use the Hungarian algorithm for optimal matching between the predicted and actual locations of tips. Experimental results demonstrate that our method achieved > 98% accuracy within 10 {\mu}m, and > 89% accuracy within 5 {\mu}m for the localisation of multi-pipette tips. The average MSE is 2.52 {\mu}m.

Rethinking Bimanual Robotic Manipulation: Learning with Decoupled Interaction Framework

Bimanual robotic manipulation is an emerging and critical topic in the robotics community. Previous works primarily rely on integrated control models that take the perceptions and states of both arms as inputs to directly predict their actions. However, we think bimanual manipulation involves not only coordinated tasks but also various uncoordinated tasks that do not require explicit cooperation during execution, such as grasping objects with the closest hand, which integrated control frameworks ignore to consider due to their enforced cooperation in the early inputs. In this paper, we propose a novel decoupled interaction framework that considers the characteristics of different tasks in bimanual manipulation. The key insight of our framework is to assign an independent model to each arm to enhance the learning of uncoordinated tasks, while introducing a selective interaction module that adaptively learns weights from its own arm to improve the learning of coordinated tasks. Extensive experiments on seven tasks in the RoboTwin dataset demonstrate that: (1) Our framework achieves outstanding performance, with a 23.5% boost over the SOTA method. (2) Our framework is flexible and can be seamlessly integrated into existing methods. (3) Our framework can be effectively extended to multi-agent manipulation tasks, achieving a 28% boost over the integrated control SOTA. (4) The performance boost stems from the decoupled design itself, surpassing the SOTA by 16.5% in success rate with only 1/6 of the model size.

Design and Benchmarking of A Multi-Modality Sensor for Robotic Manipulation with GAN-Based Cross-Modality Interpretation

In this paper, we present the design and benchmark of an innovative sensor, ViTacTip, which fulfills the demand for advanced multi-modal sensing in a compact design. A notable feature of ViTacTip is its transparent skin, which incorporates a `see-through-skin' mechanism. This mechanism aims at capturing detailed object features upon contact, significantly improving both vision-based and proximity perception capabilities. In parallel, the biomimetic tips embedded in the sensor's skin are designed to amplify contact details, thus substantially augmenting tactile and derived force perception abilities. To demonstrate the multi-modal capabilities of ViTacTip, we developed a multi-task learning model that enables simultaneous recognition of hardness, material, and textures. To assess the functionality and validate the versatility of ViTacTip, we conducted extensive benchmarking experiments, including object recognition, contact point detection, pose regression, and grating identification. To facilitate seamless switching between various sensing modalities, we employed a Generative Adversarial Network (GAN)-based approach. This method enhances the applicability of the ViTacTip sensor across diverse environments by enabling cross-modality interpretation.

TCR-GPT: Integrating Autoregressive Model and Reinforcement Learning for T-Cell Receptor Repertoires Generation

T-cell receptors (TCRs) play a crucial role in the immune system by recognizing and binding to specific antigens presented by infected or cancerous cells. Understanding the sequence patterns of TCRs is essential for developing targeted immune therapies and designing effective vaccines. Language models, such as auto-regressive transformers, offer a powerful solution to this problem by learning the probability distributions of TCR repertoires, enabling the generation of new TCR sequences that inherit the underlying patterns of the repertoire. We introduce TCR-GPT, a probabilistic model built on a decoder-only transformer architecture, designed to uncover and replicate sequence patterns in TCR repertoires. TCR-GPT demonstrates an accuracy of 0.953 in inferring sequence probability distributions measured by Pearson correlation coefficient. Furthermore, by leveraging Reinforcement Learning(RL), we adapted the distribution of TCR sequences to generate TCRs capable of recognizing specific peptides, offering significant potential for advancing targeted immune therapies and vaccine development. With the efficacy of RL, fine-tuned pretrained TCR-GPT models demonstrated the ability to produce TCR repertoires likely to bind specific peptides, illustrating RL's efficiency in enhancing the model's adaptability to the probability distributions of biologically relevant TCR sequences.

CrystalTac: 3D-Printed Vision-Based Tactile Sensor Family through Rapid Monolithic Manufacturing Technique

Recently, vision-based tactile sensors (VBTSs) have gained popularity in robotics systems. The sensing mechanisms of most VBTSs can be categorised based on the type of tactile features they capture. Each category requires specific structural designs to convert physical contact into optical information. The complex architectures of VBTSs pose challenges for traditional manufacturing techniques in terms of design flexibility, cost-effectiveness, and quality stability. Previous research has shown that monolithic manufacturing using multi-material 3D printing technology can partially address these challenges. This study introduces the CrystalTac family, a series of VBTSs designed with a unique sensing mechanism and fabricated through rapid monolithic manufacturing. Case studies on CrystalTac-type sensors demonstrate their effective performance in tasks involving tactile perception, along with impressive cost-effectiveness and design flexibility. The CrystalTac family aims to highlight the potential of monolithic manufacturing in VBTS development and inspire further research in tactile sensing and manipulation.

Adaptive Extraction Network for Multivariate Long Sequence Time-Series Forecasting

Models employing CNN architecture have made significant progress in multivariate long sequence time-series forecasting (MLSTF), particularly in modeling local time series characteristics. However, during the MLSTF process, extracting the global time series patterns and understanding the correlations among different variables are highly significant. To address this challenge, we introduce multi-resolution convolution and deformable convolution operations. By enlarging the receptive field using convolution kernels with different dilation factors to capture temporal correlation information across different resolutions, and adaptively adjusting the sampling positions through additional offset vectors, we enhance the network's ability to capture correlated features between variables. Building upon this, we propose ATVCNet, an adaptive temporal-variable convolutional network designed to effectively model the local/global temporal dependencies and inter-variable dependencies of multivariate time series. Specifically, extracting and fusing time series features at different resolutions, captures both local contextual information and global patterns in the time series. The designed inter-variable feature adaptive extraction module captures the correlation among different variables in the time series. We evaluated the performance of ATVCNet across eight real-world datasets. The results indicate that ATVCNet achieved a performance improvement of approximately 63.4% over state-of-the-art MLSTF models.

MagicTac: A Novel High-Resolution 3D Multi-layer Grid-Based Tactile Sensor

Accurate robotic control over interactions with the environment is fundamentally grounded in understanding tactile contacts. In this paper, we introduce MagicTac, a novel high-resolution grid-based tactile sensor. This sensor employs a 3D multi-layer grid-based design, inspired by the Magic Cube structure. This structure can help increase the spatial resolution of MagicTac to perceive external interaction contacts. Moreover, the sensor is produced using the multi-material additive manufacturing technique, which simplifies the manufacturing process while ensuring repeatability of production. Compared to traditional vision-based tactile sensors, it offers the advantages of i) high spatial resolution, ii) significant affordability, and iii) fabrication-friendly construction that requires minimal assembly skills. We evaluated the proposed MagicTac in the tactile reconstruction task using the deformation field and optical flow. Results indicated that MagicTac could capture fine textures and is sensitive to dynamic contact information. Through the grid-based multi-material additive manufacturing technique, the affordability and productivity of MagicTac can be enhanced with a minimum manufacturing cost of 4.76 GBP and a minimum manufacturing time of 24.6 minutes.

ViTacTip: Design and Verification of a Novel Biomimetic Physical Vision-Tactile Fusion Sensor

Tactile sensing is significant for robotics since it can obtain physical contact information during manipulation. To capture multimodal contact information within a compact framework, we designed a novel sensor called ViTacTip, which seamlessly integrates both tactile and visual perception capabilities into a single, integrated sensor unit. ViTacTip features a transparent skin to capture fine features of objects during contact, which can be known as the see-through-skin mechanism. In the meantime, the biomimetic tips embedded in ViTacTip can amplify touch motions during tactile perception. For comparative analysis, we also fabricated a ViTac sensor devoid of biomimetic tips, as well as a TacTip sensor with opaque skin. Furthermore, we develop a Generative Adversarial Network (GAN)-based approach for modality switching between different perception modes, effectively alternating the emphasis between vision and tactile perception modes. We conducted a performance evaluation of the proposed sensor across three distinct tasks: i) grating identification, ii) pose regression, and iii) contact localization and force estimation. In the grating identification task, ViTacTip demonstrated an accuracy of 99.72%, surpassing TacTip, which achieved 94.60%. It also exhibited superior performance in both pose and force estimation tasks with the minimum error of 0.08mm and 0.03N, respectively, in contrast to ViTac's 0.12mm and 0.15N. Results indicate that ViTacTip outperforms single-modality sensors.

TacFR-Gripper: A Reconfigurable Fin Ray-Based Compliant Robotic Gripper with Tactile Skin for In-Hand Manipulation

This paper introduces the TacFR-Gripper, a reconfigurable Fin Ray-based soft and compliant robotic gripper equipped with tactile skin, which can be used for dexterous in-hand manipulation tasks. This gripper can adaptively grasp objects of diverse shapes and stiffness levels. An array of Force Sensitive Resistor (FSR) sensors is embedded within the robotic finger to serve as the tactile skin, enabling the robot to perceive contact information during manipulation. We provide theoretical analysis for gripper design, including kinematic analysis, workspace analysis, and finite element analysis to identify the relationship between the gripper's load and its deformation. Moreover, we implemented a Graph Neural Network (GNN)-based tactile perception approach to enable reliable grasping without accidental slip or excessive force. Three physical experiments were conducted to quantify the performance of the TacFR-Gripper. These experiments aimed to i) assess the grasp success rate across various everyday objects through different configurations, ii) verify the effectiveness of tactile skin with the GNN algorithm in grasping, iii) evaluate the gripper's in-hand manipulation capabilities for object pose control. The experimental results indicate that the TacFR-Gripper can grasp a wide range of complex-shaped objects with a high success rate and deliver dexterous in-hand manipulation. Additionally, the integration of tactile skin with the GNN algorithm enhances grasp stability by incorporating tactile feedback during manipulations. For more details of this project, please view our website: https://sites.google.com/view/tacfr-gripper/homepage.

Attention for Robot Touch: Tactile Saliency Prediction for Robust Sim-to-Real Tactile Control

High-resolution tactile sensing can provide accurate information about local contact in contact-rich robotic tasks. However, the deployment of such tasks in unstructured environments remains under-investigated. To improve the robustness of tactile robot control in unstructured environments, we propose and study a new concept: \textit{tactile saliency} for robot touch, inspired by the human touch attention mechanism from neuroscience and the visual saliency prediction problem from computer vision. In analogy to visual saliency, this concept involves identifying key information in tactile images captured by a tactile sensor. While visual saliency datasets are commonly annotated by humans, manually labelling tactile images is challenging due to their counterintuitive patterns. To address this challenge, we propose a novel approach comprised of three interrelated networks: 1) a Contact Depth Network (ConDepNet), which generates a contact depth map to localize deformation in a real tactile image that contains target and noise features; 2) a Tactile Saliency Network (TacSalNet), which predicts a tactile saliency map to describe the target areas for an input contact depth map; 3) and a Tactile Noise Generator (TacNGen), which generates noise features to train the TacSalNet. Experimental results in contact pose estimation and edge-following in the presence of distractors showcase the accurate prediction of target features from real tactile images. Overall, our tactile saliency prediction approach gives robust sim-to-real tactile control in environments with unknown distractors. Project page: https://sites.google.com/view/tactile-saliency/.