optimization Model for Planning Precision Grasps with MultiFingered Hands Precision grasps with multifingered hands are important for precise placement and inhand manipulation tasks. Searching precision grasps on the object represented by point cloud, is challenging due to the complex object shape, highdimensionality, collision and undesired properties of the sensing and positioning. This paper proposes an optimization model to search for precision grasps with multifingered hands. The model takes noisy point cloud of the object as input and optimizes the grasp quality by iteratively searching for the palm pose and finger joints positions. The collision between the hand and the object is approximated and penalized by a series of leastsquares. The collision approximation is able to handle the point cloud representation of the objects with complex shapes. The proposed optimization model is able to locate collisionfree optimal precision grasps efficiently. The average computation time is 0.50 secgrasp. The searching is robust to the incompleteness and noise of the point cloud. The effectiveness of the algorithm is demonstrated by experiments.

Robust Grasp Planning Over Uncertain Shape Completions We present a method for planning robust grasps over uncertain shape completed objects. For shape completion, a deep neural network is trained to take a partial view of the object as input and outputs the completed shape as a voxel grid. The key part of the network is dropout layers which are enabled not only during training but also at runtime to generate a set of shape samples representing the shape uncertainty through Monte Carlo sampling. Given the set of shape completed objects, we generate grasp candidates on the mean object shape but evaluate them based on their joint performance in terms of analytical grasp metrics on all the shape candidates. We experimentally validate and benchmark our method against another stateoftheart method with a Barrett hand on 90000 grasps in simulation and 200 grasps on a real Franka Emika Panda. All experimental results show statistically significant improvements both in terms of grasp quality metrics and grasp success rate, demonstrating that planning shapeuncertaintyaware grasps brings significant advantages over solely planning on a single shape estimate, especially when dealing with complex or unknown objects.