Recent research shows its great potential by preparing and learning from large-scale synthetic datasets.

Real-time interactive grasp synthesis for dynamic objects remains challenging as existing methods fail to achieve low-latency inference while maintaining promptability. To bridge this gap, we propose SPGrasp (spatiotemporal prompt-driven dynamic grasp synthesis), a novel framework extending segment anything model v2 (SAMv2) for video stream grasp estimation. Our core innovation integrates user prompts with spatiotemporal context, enabling real-time interaction with end-to-end latency as low as 59 ms while ensuring temporal consistency for dynamic objects. In benchmark evaluations, SPGrasp achieves instance-level grasp accuracies of 90.6% on OCID and 93.8% on Jacquard. On the challenging GraspNet-1Billion dataset under continuous tracking, SPGrasp achieves 92.0% accuracy with 73.1 ms per-frame latency, representing a 58.5% reduction compared to the prior state-of-the-art promptable method RoG-SAM while maintaining competitive accuracy. Real-world experiments involving 13 moving objects demonstrate a 94.8% success rate in interactive grasping scenarios. These results confirm SPGrasp effectively resolves the latency-interactivity trade-off in dynamic grasp synthesis.

This work proposes a learning method to accelerate robotic pick-and-place planning by predicting shared grasps. Shared grasps are defined as grasp poses feasible to both the initial and goal object configurations in a pick-and-place task. Traditional analytical methods for solving shared grasps evaluate grasp candidates separately, leading to substantial computational overhead as the candidate set grows. To overcome the limitation, we introduce an Energy-Based Model (EBM) that predicts shared grasps by combining the energies of feasible grasps at both object poses. This formulation enables early identification of promising candidates and significantly reduces the search space. Experiments show that our method improves grasp selection performance, offers higher data efficiency, and generalizes well to unseen grasps and similarly shaped objects.

Grasping unknown objects in unstructured environments remains a fundamental challenge in robotics, requiring both semantic understanding and spatial reasoning. Existing methods often rely on dense training datasets or explicit geometric modeling, limiting their scalability to real-world tasks. Recent advances in Large Multimodal Models (LMMs) offer new possibilities for integrating vision and language understanding, but their application to autonomous robotic grasping remains largely unexplored. We present ORACLE-Grasp, a zero-shot framework that leverages LMMs as semantic oracles to guide grasp selection without requiring additional training or human input. The system formulates grasp prediction as a structured, iterative decision process, using dual-prompt tool calling to first extract high-level object context and then select task-relevant grasp regions. By discretizing the image space and reasoning over candidate areas, ORACLE-Grasp mitigates the spatial imprecision common in LMMs and produces human-like, task-driven grasp suggestions. Early stopping and depth-based refinement steps further enhance efficiency and physical grasp reliability. Experiments demonstrate that the predicted grasps achieve low positional and orientation errors relative to human-annotated ground truth and lead to high success rates in real-world pick up tasks. These results highlight the potential of combining language-driven reasoning with lightweight vision techniques to enable robust, autonomous grasping without task-specific datasets or retraining.

-- Transparent object grasping remains a persistent challenge in robotics, largely due to the difficulty of acquiring precise 3D information. Conventional optical 3D sensors struggle to capture transparent objects, and machine learning methods are often hindered by their reliance on high-quality datasets. Leveraging NeRF's capability for continuous spatial opacity modeling, our proposed architecture integrates a NeRF-based approach for reconstructing the 3D information of transparent objects. Despite this, certain portions of the reconstructed 3D information may remain incomplete. T o address these deficiencies, we introduce a shape-prior-driven completion mechanism, further refined by a geometric pose estimation method we have developed. This allows us to obtain a complete and reliable 3D information of transparent objects. Utilizing this refined data, we perform scene-level grasp prediction and deploy the results in real-world robotic systems. Experimental validation demonstrates the efficacy of our architecture, showcasing its capability to reliably capture 3D information of various transparent objects in cluttered scenes, and correspondingly, achieve high-quality, stable, and executable grasp predictions.

Grasp detection requires flexibility to handle objects of various shapes without relying on prior knowledge of the object, while also offering intuitive, user-guided control. This paper introduces GraspSAM, an innovative extension of the Segment Anything Model (SAM), designed for prompt-driven and category-agnostic grasp detection. Unlike previous methods, which are often limited by small-scale training data, GraspSAM leverages the large-scale training and prompt-based segmentation capabilities of SAM to efficiently support both target-object and category-agnostic grasping. By utilizing adapters, learnable token embeddings, and a lightweight modified decoder, GraspSAM requires minimal fine-tuning to integrate object segmentation and grasp prediction into a unified framework. The model achieves state-of-the-art (SOTA) performance across multiple datasets, including Jacquard, Grasp-Anything, and Grasp-Anything++. Extensive experiments demonstrate the flexibility of GraspSAM in handling different types of prompts (such as points, boxes, and language), highlighting its robustness and effectiveness in real-world robotic applications.

Many robotic applications require to grasp objects not arbitrarily but at a very specific object part. This is especially important for manipulation tasks beyond simple pick-and-place scenarios or in robot-human interactions, such as object handovers. We propose AnyPart, a practical system that combines open-vocabulary object detection, open-vocabulary part segmentation and 6DOF grasp pose prediction to infer a grasp pose on a specific part of an object in 800 milliseconds. We contribute two new datasets for the task of open-vocabulary part-based grasping, a hand-segmented dataset containing 1014 object-part segmentations, and a dataset of real-world scenarios gathered during our robot trials for individual objects and table-clearing tasks. We evaluate AnyPart on a mobile manipulator robot using a set of 28 common household objects over 360 grasping trials. AnyPart is capable of producing successful grasps 69.52 %, when ignoring robot-based grasp failures, AnyPart predicts a grasp location on the correct part 88.57 % of the time.

Accurate grasping is the key to several robotic tasks including assembly and household robotics. Executing a successful grasp in a cluttered environment requires multiple levels of scene understanding: First, the robot needs to analyze the geometric properties of individual objects to find feasible grasps. These grasps need to be compliant with the local object geometry. Second, for each proposed grasp, the robot needs to reason about the interactions with other objects in the scene. Finally, the robot must compute a collision-free grasp trajectory while taking into account the geometry of the target object. Most grasp detection algorithms directly predict grasp poses in a monolithic fashion, which does not capture the composability of the environment. In this paper, we introduce an end-to-end architecture for object-centric grasping. The method uses pointcloud data from a single arbitrary viewing direction as an input and generates an instance-centric representation for each partially observed object in the scene. This representation is further used for object reconstruction and grasp detection in cluttered table-top scenes. We show the effectiveness of the proposed method by extensively evaluating it against state-of-the-art methods on synthetic datasets, indicating superior performance for grasping and reconstruction. Additionally, we demonstrate real-world applicability by decluttering scenes with varying numbers of objects.

Abstract-- Robotic manipulation systems operating in complex environments rely on perception systems which provide information about the geometry (pose and 3D shape) of the objects in the scene along with other semantic information such as object labels. This information is then used for choosing the feasible grasps on relevant objects. In this paper, we present a novel method to provide this geometric and semantic information of all objects in the scene as well as feasible grasps on those objects simultaneously. The main advantage of our method is its speed as it avoids sequential perception and grasp planning steps. With detailed quantitative analysis we show that our method delivers competitive performance compared to the state-of-the-art dedicated methods for object shape, pose, and grasp predictions, while providing fast inference at 30 frames per second speed.