Grasp Multiple Objects with One Hand

Abstract

The intricate kinematics of the human hand enable simultaneous grasping and manipulation of multiple objects, essential for tasks such as object transfer and in-hand manipulation. Despite its significance, the domain of robotic multi-object grasping is relatively unexplored and presents notable challenges in kinematics, dynamics, and object configurations. This paper introduces MultiGrasp, a novel two-stage approach for multi-object grasping using a dexterous multi-fingered robotic hand on a tabletop. The process consists of (i) generating pre-grasp proposals and (ii) executing the grasp and lifting the objects. Our experimental focus is primarily on dual-object grasping, achieving a success rate of 44.13%, highlighting adaptability to new object configurations and tolerance for imprecise grasps. Additionally, the framework demonstrates the potential for grasping more than two objects at the cost of inference speed.

References in corpus (6)

• Learning agile and dynamic motor skills for legged robots
• Learning Quadrupedal Locomotion over Challenging Terrain
• Learning robust perceptive locomotion for quadrupedal robots in the wild
• DTC: Deep Tracking Control
• Synthesizing Diverse and Physically Stable Grasps with Arbitrary Hand Structures using Differentiable Force Closure Estimator
• Exploiting Kinematic Redundancy for Robotic Grasping of Multiple Objects

Cited by in corpus (6)

• Embedding high-resolution touch across robotic hands enables adaptive human-like grasping
• Tac-Man: Tactile-Informed Prior-Free Manipulation of Articulated Objects
• A Survey of Embodied Learning for Object-Centric Robotic Manipulation
• A Unification Between Deep-Learning Vision, Compartmental Dynamical Thermodynamics, and Robotic Manipulation for a Circular Economy
• Bimanual Grasp Synthesis for Dexterous Robot Hands
• Grasping a Handful: Sequential Multi-Object Dexterous Grasp Generation