Technology Category
Intelligent Robots: ManipulationSearch and Optimization: Sampling/Simulation-based SearchPlanning, Routing, and Scheduling: Replanning and Plan Repair
QuickGrasp: Lightweight Antipodal Grasp Planning with Point Clouds
🤖 AI Summary
To address poor generalization, insufficient real-time performance, and low repeatability of robotic grasping planning in complex scenes, this paper proposes a lightweight analytical antipodal grasping method. Our approach abandons conventional 6-DOF pose sampling and instead directly optimizes antipodal contact points on object surfaces, establishing a sampling-free or low-sampling nonlinear optimization framework. We introduce a soft region-growing surface segmentation algorithm to enhance geometric understanding robustness for irregular objects. Additionally, we design an analytical grasp quality metric based on indirect force closure. Evaluated in simulation and on a real UR5 robot equipped with a Robotiq gripper, our method achieves a 3.2× speedup in planning time and an 18.7% improvement in success rate over GPD. It demonstrates strong cross-domain generalization capability and suitability for real-time deployment.
Technology Category
Application Category
📝 Abstract
Grasping has been a long-standing challenge in facilitating the final interface between a robot and the environment. As environments and tasks become complicated, the need to embed higher intelligence to infer from the surroundings and act on them has become necessary. Although most methods utilize techniques to estimate grasp pose by treating the problem via pure sampling-based approaches in the six-degree-of-freedom space or as a learning problem, they usually fail in real-life settings owing to poor generalization across domains. In addition, the time taken to generate the grasp plan and the lack of repeatability, owing to sampling inefficiency and the probabilistic nature of existing grasp planning approaches, severely limits their application in real-world tasks. This paper presents a lightweight analytical approach towards robotic grasp planning, particularly antipodal grasps, with little to no sampling in the six-degree-of-freedom space. The proposed grasp planning algorithm is formulated as an optimization problem towards estimating grasp points on the object surface instead of directly estimating the end-effector pose. To this extent, a soft-region-growing algorithm is presented for effective plane segmentation, even in the case of curved surfaces. An optimization-based quality metric is then used for the evaluation of grasp points to ensure indirect force closure. The proposed grasp framework is compared with the existing state-of-the-art grasp planning approach, Grasp pose detection (GPD), as a baseline over multiple simulated objects. The effectiveness of the proposed approach in comparison to GPD is also evaluated in a real-world setting using image and point-cloud data, with the planned grasps being executed using a ROBOTIQ gripper and UR5 manipulator.
Problem
Research questions and friction points this paper is trying to address.
Lightweight antipodal grasp planning with point clouds
Overcoming poor generalization in real-life robotic grasping
Reducing time and improving repeatability in grasp planning
Innovation
Methods, ideas, or system contributions that make the work stand out.
Lightweight analytical grasp planning without sampling
Soft-region-growing algorithm for plane segmentation
Optimization-based quality metric for grasp evaluation
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