← Back to Benchmarks
simmediumgraspingmetric · varies
DG16M: A Large-Scale Dataset for Dual-Arm Grasping with Force-Optimized Grasps
Description
Dual-arm robotic grasping is crucial for handling large objects that require stable and coordinated manipulation. While single-arm grasping has been extensively studied, datasets tailored for dual-arm settings remain scarce. We introduce a large-scale dataset of 16 million dual-arm grasps, evaluated under improved force-closure constraints. Additionally, we develop a benchmark dataset containing 300 objects with approximately 30,000 grasps, evaluated in a physics simulation environment, providin