2 research outputs found
A Learning Framework for Robust Bin Picking by Customized Grippers
Customized grippers have specifically designed fingers to increase the
contact area with the workpieces and improve the grasp robustness. However,
grasp planning for customized grippers is challenging due to the object
variations, surface contacts and structural constraints of the grippers. In
this paper, we propose a learning framework to plan robust grasps for
customized grippers in real-time. The learning framework contains a low-level
optimization-based planner to search for optimal grasps locally under object
shape variations, and a high-level learning-based explorer to learn the grasp
exploration based on previous grasp experience. The optimization-based planner
uses an iterative surface fitting (ISF) to simultaneously search for optimal
gripper transformation and finger displacement by minimizing the surface
fitting error. The high-level learning-based explorer trains a region-based
convolutional neural network (R-CNN) to propose good optimization regions,
which avoids ISF getting stuck in bad local optima and improves the collision
avoidance performance. The proposed learning framework with RCNN-ISF is able to
consider the structural constraints of the gripper, learn grasp exploration
strategy from previous experience, and plan optimal grasps in clutter
environment in real-time. The effectiveness of the algorithm is verified by
experiments.Comment: Submitted to 2019 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS 2019). arXiv admin note: text overlap with
arXiv:1803.1129
Efficient Grasp Planning and Execution with Multi-Fingered Hands by Surface Fitting
This paper introduces a framework to plan grasps with multi-fingered hands.
The framework includes a multi-dimensional iterative surface fitting (MDISF)
for grasp planning and a grasp trajectory optimization (GTO) for grasp
imagination. The MDISF algorithm searches for optimal contact regions and hand
configurations by minimizing the collision and surface fitting error, and the
GTO algorithm generates optimal finger trajectories to reach the highly ranked
grasp configurations and avoid collision with the environment. The proposed
grasp planning and imagination framework considers the collision avoidance and
the kinematics of the hand-robot system, and is able to plan grasps and
trajectories of different categories efficiently with gradient-based methods
using the captured point cloud. The found grasps and trajectories are robust to
sensing noises and underlying uncertainties. The effectiveness of the proposed
framework is verified by both simulations and experiments.Comment: accepted to RAL2019 with IROS option. 8 page