Neural Grasp Distance Fields for Robot Manipulation

Abstract-- We formulate grasp learning as a neural field and present Neural Grasp Distance Fields (NGDF). Here, the input is a 6D pose of a robot end effector and output is a distance to a continuous manifold of valid grasps for an object. In contrast to current approaches that predict a set of discrete candidate grasps, the distance-based NGDF representation is easily interpreted as a cost, and minimizing this cost produces a successful grasp pose. This grasp distance cost can be incorporated directly into a trajectory optimizer for joint optimization with other costs such as trajectory smoothness and collision avoidance. Figure 1: (a) Existing grasp estimation methods produce discrete grasp We evaluate NGDF on joint grasp and motion planning in sets which do not represent the true continuous manifold of possible simulation and the real world, outperforming baselines by 63% grasps. This distance can be leveraged as a cost for optimization, facilitating joint grasp and motion planning. We present Neural Grasp Distance Fields (NGDF), which optimization results in a smooth, collision-free trajectory that model the continuous manifold of valid grasp poses as the reaches a valid grasp pose.