Robotic grasping is facing a variety of real-world uncertainties caused by non-static object states, unknown object properties, and cluttered object arrangements. The difficulty of grasping increases with the presence of more uncertainties, where commonly used learning-based approaches struggle to perform consistently across varying conditions. In this study, we integrate the idea of similarity matching to tackle the challenge of grasping novel objects that are simultaneously in motion and densely cluttered using a single RGBD camera, where multiple uncertainties coexist. We achieve this by shifting visual detection from global to local states and operating grasp planning from static to dynamic scenes. Notably, we introduce optimization methods to enhance planning efficiency for this time-sensitive task. Our proposed system can adapt to various object types, arrangements and movement speeds without the need for extensive training, as demonstrated by real-world experiments.

Grasping unknown objects from a single view has remained a challenging topic in robotics due to the uncertainty of partial observation. Recent advances in large-scale models have led to benchmark solutions such as GraspNet-1Billion. However, such learning-based approaches still face a critical limitation in performance robustness for their sensitivity to sensing noise and environmental changes. To address this bottleneck in achieving highly generalized grasping, we abandon the traditional learning framework and introduce a new perspective: similarity matching, where similar known objects are utilized to guide the grasping of unknown target objects. We newly propose a method that robustly achieves unknown-object grasping from a single viewpoint through three key steps: 1) Leverage the visual features of the observed object to perform similarity matching with an existing database containing various object models, identifying potential candidates with high similarity; 2) Use the candidate models with pre-existing grasping knowledge to plan imitative grasps for the unknown target object; 3) Optimize the grasp quality through a local fine-tuning process. To address the uncertainty caused by partial and noisy observation, we propose a multi-level similarity matching framework that integrates semantic, geometric, and dimensional features for comprehensive evaluation. Especially, we introduce a novel point cloud geometric descriptor, the C-FPFH descriptor, which facilitates accurate similarity assessment between partial point clouds of observed objects and complete point clouds of database models. In addition, we incorporate the use of large language models, introduce the semi-oriented bounding box, and develop a novel point cloud registration approach based on plane detection to enhance matching accuracy under single-view conditions. Real-world experiments demonstrate that our proposed method significantly outperforms existing benchmarks in grasping a wide variety of unknown objects in both isolated and cluttered scenarios, showcasing exceptional robustness across varying object types and operating environments.

Robotic waste sorting poses significant challenges in both perception and manipulation, given the extreme variability of objects that should be recognized on a cluttered conveyor belt. While deep learning has proven effective in solving complex tasks, the necessity for extensive data collection and labeling limits its applicability in real-world scenarios like waste sorting. To tackle this issue, we introduce a data augmentation method based on a novel GAN architecture called wasteGAN. The proposed method allows to increase the performance of semantic segmentation models, starting from a very limited bunch of labeled examples, such as few as 100. The key innovations of wasteGAN include a novel loss function, a novel activation function, and a larger generator block. Overall, such innovations helps the network to learn from limited number of examples and synthesize data that better mirrors real-world distributions. We then leverage the higher-quality segmentation masks predicted from models trained on the wasteGAN synthetic data to compute semantic-aware grasp poses, enabling a robotic arm to effectively recognizing contaminants and separating waste in a real-world scenario. Through comprehensive evaluation encompassing dataset-based assessments and real-world experiments, our methodology demonstrated promising potential for robotic waste sorting, yielding performance gains of up to 5.8% in picking contaminants. The project page is available at https://github.com/bach05/wasteGAN.git.

To achieve recycling of mixed industrial waste toward an advanced sustainable society, waste sorting automation through robots is crucial and urgent. For this purpose, a robot is required to recognize the category, shape, pose, and condition of different waste items and manipulate them according to the category to be sorted. This survey considers three potential difficulties in the sorting automation: 1) End-effector: to robustly grasp and manipulate different waste items with dirt and deformations; 2) Sensor: to recognize the category, shape, and pose of existing objects to be manipulated and the wet and dirty conditions of their surfaces; and 3) Planner: to generate feasible and efficient sequences and trajectories. This survey includes 76 references to studies related to automatic waste sorting and 159 references to worldwide waste recycling attempts. This pioneering investigation reveals the possibility and limitations of conventional systems; thus, providing insights on open issues and potential technologies to achieve a robot-incorporated sorter for the chaotic mixed waste is one of its contributions. This paper further presents a system design policy for readers and discusses future advanced sorters, thereby contributing to the field of robotics and automation.Note to Practitioners—Most automated sorting systems operate for limited target waste items. This study is motivated by the automation of mixed industrial waste treatment facilities using advanced robotic sorters. Emerging advances and increasing functionalities of robot system components will widen system applicability and increase use cases in the chaotic mixed industrial waste domain. This paper surveys the research conducted to date, discusses open issues and potential approaches, and presents user guides that provide practitioners with a system design policy. The user guides created according to the strengths and weaknesses of each system configuration provide future researchers and developers with a useful a priori design policy that has been thus far validated on efficiency, quality, productivity, and reliability. A question-and-answer style guide and a sorting-target-aware previous study reference list allows users to find the desired system configuration, including the investigated components according to their purpose.

To safely and efficiently extract an object from the clutter, this paper presents a bimanual manipulation planner in which one hand of the robot is used to slide the target object out of the clutter while the other hand is used to support the surrounding objects to prevent the clutter from collapsing. Our method uses a neural network to predict the physical phenomena of the clutter when the target object is moved. We generate the most efficient action based on the Monte Carlo tree search. The grasping and sliding actions are planned to minimize the number of motion sequences to pick the target object. In addition, the object to be supported is determined to minimize the position change of surrounding objects. Experiments with a real bimanual robot confirmed that the robot could retrieve the target object, reducing the total number of motion sequences and improving safety.

When humans want to understand an object’s 3D shape, they watch the object from different viewpoints. Changing the viewpoint is either performed actively, i.e., moving eye sights or the human head, or passively, i.e., holding and reposing the object. Inspired by the humans’ passive policy, we propose a method to plan the motion for a dual-arm robot to hold and repose an object, capture multiple views using a stationary depth sensor mounted on the robot head, and obtain the object’s 3D shape from the multiple views. Primarily, we develop algorithms to determine the Next Best Configuration (NBC) for observation and Next Best Regrasp/Grasp (NBR/G) poses while considering elements like the confidence of captured partial point clouds, robotic manipulability, robotic motion distances, and sensing ranges. We study the necessity and influence of these elements on the time costs and surface coverage quality in the experimental section using several representative objects. The results show that the elements play essential roles in supporting specific actions or suppressing certain costs. They help to secure efficient robot motion and satisfactory 3D shape recovery quality.Note to Practitioners—This paper is motivated by the difficulties in using commercial 3D scanners. A commercial 3D scanner set usually comprises a scanning sensor, a rotating table, and editing software. To scan the 3D shape of an object, a human needs to place the object on the rotating table with different poses, let the scanner obtain several partial point clouds, and use the editing software to merge them into a final model. The human must carefully design the different poses by considering both the object’s self obstructions and stable placements, which is tiring and difficult to be applied to large-scale tasks like building 3D shape databases containing many objects. On the other hand, although several robotic solutions exist for automatic scanning, they either use an eye-in-hand scanner to scan a stationary object or an arm to hold and move an object for scanning. In the former case, the bottom or downward faces of the object cannot be covered. In the latter case, the surface blocked by the fingers of the holding hand will be lost. The method proposed by this paper plans dual-arm robot motion to grasp and move objects for scanning. It automatically determines pick-up, rotation, and handover to maximize scanning coverage. Compared with commercial scanners and existing robotic solutions, the method performs automatic scanning with high coverage and is more advantageous for scanning many objects without human intervention.

For assembly tasks, it is essential to fix target parts firmly and accurately estimate their poses. Several rigid jigs for individual parts are frequently used in assembly factories to achieve a precise and time-efficient product assembly. However, providing customized jigs is time-consuming. In this study, to address the lack of versatility in the shapes for which jigs can be used, we developed a flexible jig with a soft membrane including transparent beads and oil with a tuned refractive index. The bead-based jamming transition was accomplished by discharging only the oil, enabling the part to be firmly fixed. Because the two cameras under the jig can capture membrane shape changes, we proposed a sensing method to estimate the orientation of the part based on the behaviors of markers created on the jig's inner surface. Through estimation experiments, the proposed system can estimate the orientation of a cylindrical object with a diameter larger than 50 mm and an RMSE of less than 3°.

When generating robot motions based on instructions such as cooking recipes, ambiguity of the instructions and lack of necessary information are problematic for the robot. To solve this problem, we propose an efficient motion planning approach for a dual-arm robot by constructing a graph repre-senting a motion sequence based on a recipe consisting of verbal instructions and cooking images. A functional unit is generated based on the linguistic instructions in the recipe. Since most recipes lack the necessary information for executing the motion, we first consider extracting the information about the cooking motion like cutting from the food images of the recipe and supplementing it. In addition, to supplement the actions that humans perform unconsciously, we generate functional units for actions not explicitly mentioned in the recipe based on the current situation of the cooking process, and then connect them to the functional units generated from the recipe. Moreover, during the connection we consider the motion of the robot's arms in parallel for an efficient execution of the recipe, similar to those of a human. Through experiments, we demonstrate that for a given recipe, the proposed method can be used to generate a cooking sequence with the supplementary information needed, and executed by a dual-arm robot. The results show that the proposed method is effective and can simplify robot teaching in cooking tasks.

To design a general-purpose assembly robot system that can handle objects of various shapes, we propose a soft jig that fits to the shapes of assembly parts. The functionality of the soft jig is based on a jamming gripper developed in the field of soft robotics. The soft jig has a bag covered with a malleable silicone membrane, which has high friction, elongation, and contraction rates for keeping parts fixed. The bag is filled with glass beads to achieve a jamming transition. We propose a method to configure parts-fixing on the soft jig based on contact relations, reachable directions, and the center of gravity of the parts that are fixed on the jig. The usability of the soft jig was evaluated in terms of the fixing performance and versatility for various shapes and postures of parts.

To generate assembly sequences that robots can easily handle, this study tackled assembly sequence generation (ASG) by considering two tradeoff objectives: (1) insertion conditions and (2) degrees of the constraints affecting the assembled parts. We propose a multi-objective genetic algorithm to balance these two objectives. Furthermore, we extend our previously proposed 3D computer-aided design (CAD)-based method for extracting three types of two-part relationship matrices from 3D models that include deformable parts. The interference between deformable and other parts can be determined using scaled part shapes. Our proposed ASG can produce Pareto-optimal sequences for multi-component models with deformable parts such as rubber bands, rubber belts, and roller chains. We further discuss the limitation and applicability of the generated sequences to robotic assembly.