arXiv:2607. 17757v1 Announce Type: cross Abstract: Current bin picking methods that rely heavily on end-to-end learning often falter when confronted with unfamiliar or complex objects in unstructured environments.
In logistics automation, precise segmentation of unseen objects is crucial for efficient robotic manipulation in cluttered environments. Tasks such as bin-picking and shelf-picking require robust perception to handle occlusions, varying object shapes, and complex spatial arrangements.
arXiv:2607. 17754v1 Announce Type: cross Abstract: In logistics automation, precise segmentation of unseen objects is crucial for efficient robotic manipulation in cluttered environments.
arXiv:2603. 07866v3 Announce Type: replace-cross Abstract: Offshore inspection and maintenance have increasingly been using legged robots for routine sensing, yet many useful interventions still require physical interaction with tools, containers, and task-relevant objects.
arXiv:2604. 04690v2 Announce Type: replace-cross Abstract: Bin picking in real industrial environments remains challenging due to severe clutter, occlusions, and the high cost of traditional 3D sensing setups.
arXiv:2608. 00946v1 Announce Type: cross Abstract: Existing 6-DoF grasp detectors typically rank grasp candidates by detector confidence.
arXiv:2606. 17054v1 Announce Type: cross Abstract: Humans can grasp objects effortlessly, whereas multi-fingered robots are far from this level of generality.
MultiGraspNet is a multitask 3D vision model that simultaneously predicts feasible poses for both parallel and vacuum grippers, allowing a single robot to handle multiple end effectors. Trained on the aligned GraspNet-1Billion and SuctionNet-1Billion datasets, it generates graspability masks that quantify the suitability of each scene point for successful grasps. With only 15.75 M parameters, the model achieves fast inference on a single GPU and demonstrates competitive performance against single-task models while reducing computational cost, as shown in extensive experiments and real‑world tests on a single‑arm multi‑gripper setup.
The paper introduces an interaction‑centric framework that unifies representations for two‑finger gripper manipulation across different robot embodiments. By using a parameterized universal gripper abstraction and a canonical gripper‑frame representation, the system infers sub‑tasks from language and RGB‑D inputs, grounds interaction triplets, and employs hybrid features and a Flow‑Matching Transformer to generate smooth 7‑DoF action sequences. Experiments in both simulation and real‑world settings show that this approach achieves competitive benchmark performance while enabling extreme cross‑embodiment and cross‑viewpoint zero‑shot sim‑to‑real transfer to heterogeneous robot platforms.
In recent years, there has been growing interest in robust robotic systems for precise bin-picking applications. To achieve reliable performance, such systems must address errors arising from both the object pose estimation and the grasping process.
arXiv:2605. 31286v2 Announce Type: replace-cross Abstract: Real-world household robots require Vision-Language-Action (VLA) foundation models that can acquire reusable manipulation skills across diverse objects, task conditions, and household environments.
The paper presents a perception-action pipeline for robotic pick‑and‑place in convenience stores, using annotation‑guided visual prompting to identify pickable objects and placement locations via bounding boxes. It replaces traditional step‑by‑step planning with Action Chunking with Transformers (ACT), an imitation learning algorithm that predicts chunked action sequences from human demonstrations. The system is evaluated on success rate and visual analysis of grasping behavior, showing improved grasp accuracy and adaptability in retail environments.
arXiv:2606. 12910v1 Announce Type: cross Abstract: For robotics to be effectively integrated into household or industrial environments, machines must adapt to natural-language prompts in real time.