Accurately recognizing the structural regions of targeted objects is crucial for successful manipulation. In this study, we concentrate on the task of hanging crumpled garments on a rack, a common scenario in household environments. This context presents two primary challenges: (1) perceiving and grasping the structural regions of garments that exhibit severe deformations and self-occlusions; (2) adjusting the configuration of garments to fit the supporting components of the rack. To address these challenges, we propose a confidence-guided grasping strategy that actively seeks garment collars through handovers between dual robotic arms. In particular, we develop an autonomous data collection procedure in real-world settings to train the collar detection network. The exact grasping pose is determined through depth-aware contour extraction, and its success is evaluated based on a specially designed metric. Furthermore, we formulate the hanging task as one-shot imitation learning with an egocentric view. To precisely align the collar with the supporting item, we propose a two-step hanging strategy that involves coarse approaching followed by fine transformation. We perform comprehensive experiments and show that our framework notably enhances the success rate compared to existing methods.

Ultrasound-based medical examination usually requires establishing proper contact between an ultrasound probe and a human body that ensures the quality of ultrasound images. The scanning skills are quite challenging for a robot to learn primarily due to the complex coupling between the applied force profile and the resulting ultrasound image quality. While reinforcement learning appears as a powerful tool for learning complex robot skills, the deployment of these algorithms in medical robots demands special attention due to the evident safety concerns that arise from physical probe-tissue interactions. In this paper, we explicitly consider external constraints on the force magnitude when searching for the optimal policy parameters to enhance safety during ultrasound-guided robotic interventions. In particular, we study policy optimization under the framework of a constrained Markov decision process. The resulting gradient-based policy update is then subject to the involved constraints, which can be readily addressed by the primal-dual interior-point technique. In addition, upon the observation that policy update requires consecutive policies to be close to each other to have stable and robust performance with reinforcement learning algorithms, we design the learning rate of policy gradient from an imitation perspective. The performance of the proposed constraint-aware policy optimization method is validated with experiments of robotic ultrasound imaging for spinal diagnosisNote to Practitioners—This paper was motivated by the problem of safely learning the optimal interaction force strategy to facilitate robotic ultrasound imaging. Existing approaches to robotic ultrasound imaging usually empirically set a constant value for the scanning force, despite the fact the force strategy plays an important role in the quality of the ultrasound images. This paper suggests the usage of reinforcement learning to identify the optimal interaction force due to the complex acoustic coupling between the force and the ultrasound image quality. Specifically, we propose constraint-aware reinforcement learning in view of the safety-critical issues as a result of physical human-probe interaction. We then conduct a theoretical analysis of the proposed safe reinforcement learning, including monotonic improvement and policy value bound under mild assumptions. Preliminary real experiments on ultrasound imaging of the spine of a phantom for scoliosis assessment suggest that the proposed approach can safely learn the optimal scanning force without violating the prescribed force threshold. In the future, we would like to apply our approach to learning the optimal scanning force on different organs of interest of human subjects.

The challenges inherent in long-horizon tasks in robotics persist due to the typical inefficient exploration and sparse rewards in traditional reinforcement learning approaches. To address these challenges, we have developed a novel algorithm, termed hlexplicit-implicit subgoal planning (EISP), designed to tackle long-horizon tasks through a divide-and-conquer approach. We utilize two primary criteria, feasibility and optimality, to ensure the quality of the generated subgoals. EISP consists of three components: a hybrid subgoal generator, a hindsight sampler, and a value selector. The hybrid subgoal generator uses an explicit model to infer subgoals and an implicit model to predict the final goal, inspired by way of human thinking that infers subgoals by using the current state and final goal as well as reason about the final goal conditioned on the current state and given subgoals. Additionally, the hindsight sampler selects valid subgoals from an offline dataset to enhance the feasibility of the generated subgoals. While the value selector utilizes the value function in reinforcement learning to filter the optimal subgoals from subgoal candidates. To validate our method, we conduct four long-horizon tasks in both simulation and the real world. The obtained quantitative and qualitative data indicate that our approach achieves promising performance compared to other baseline methods. These experimental results can be seen on the website https://sites.google.com/view/vaesi.

Garment folding is a ubiquitous domestic task that is difficult to automate due to the highly deformable nature of fabrics. In this article, we propose a novel method of learning from demonstrations that enables robots to autonomously manipulate an assistive tool to fold garments. In contrast to traditional methods (that rely on low-level pixel features), our proposed solution uses a dense visual descriptor to encode the demonstration into a high-levelhand-object graph(HoG) that allows to efficiently represent the interactions between the manipulated tool and robots. With that, we leverage graph neural network to autonomously learn the forward dynamics model from HoGs, then, given only a single demonstration, the imitation policy is optimized with a model predictive controller to accomplish the folding task. To validate the proposed approach, we conducted a detailed experimental study on a robotic platform instrumented with vision sensors and a custom-made end-effector that interacts with the folding board.

In this paper, we present a robotic system to automatically perform defect inspection tasks over free-form specular surfaces, which the image acquisition sub-system is equipped with a 6-DOF robot manipulator to achieve flexible scanning. Given the mesh model of the workpiece, we implement K-means based region segmentation algorithm on the point cloud after preprocessing. Then, we take the smooth regions as input to plan the scanning path. A projection registration method that robustly localizes the object in the robot’s frame is proposed for real-time workpiece localization. According to the optical features of the high-resolution line scan, we design an image processing pipeline to detect defects from the captured images. We report a detailed experimental study to validate the proposed methodology.