Grasping Trajectory Optimization with Point Clouds

Informatics in Control, Automation and Robotics, 2019

The task of robotic grasping brings together several challenges. Among them, we focus on the calculus of where gripper plates should be placed over an object's surface in order to grasp it. To do this, we have developed a method based on visual information. The main goal is to geometrically analyse a single 3D point cloud, where the object is present, to find the best pair of contact points so a gripper can perform a stable grasp of the object. Our proposal is to find these points near a perpendicular cutting plane to the object's main axis through its centroid. We have found that this method shows promising experimental results fast and accurate enough to be used on real service robots.

IEEE Robotics and Automation Letters

This paper introduces a framework to plan grasps with multi-fingered hands. The framework includes a multidimensional 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. The experimental videos are available at [1].

2010 IEEE International Conference on Robotics and Automation, 2010

In this work, we present an integrated planner for collision-free single and dual arm grasping motions. The proposed Grasp-RRT planner combines the three main tasks needed for grasping an object: finding a feasible grasp, solving the inverse kinematics and searching a collision-free trajectory that brings the hand to the grasping pose. Therefore, RRTbased algorithms are used to build a tree of reachable and collision-free configurations. During RRT-generation, potential grasping positions are generated and approach movements toward them are computed. The quality of reachable grasping poses is scored with an online grasp quality measurement module which is based on the computation of applied forces in order to diminish the net torque. We also present an extension to a dual arm planner which generates bimanual grasps together with corresponding dual arm grasping motions. The algorithms are evaluated with different setups in simulation and on the humanoid robot ARMAR-III.

Applied Sciences

In recent years, technological developments in the field of robotics have expanded their application spectrum to encompass tasks that involve human inclusion in the same workspace. One of the challenges of robotics collaboration is the issue of how a robot and a human can perform daily collaborative tasks, like manipulation of an object. One significant specific problem to solve is where the robot can grasp the object knowing the human grasping points. This research proposes a planning algorithm to find a robot grasping point based on geometric grasp metrics as well as a new heuristic metric focused on the intrinsic inertia in multi-directional object movement. We propose three grasping points: two points emulating each human hand, positioned anywhere on the object and one last point, referencing the robot, which will be optimized as a multi-objective (MO) function problem. The planner was tested using common objects present in human environments (a chair and a table).

IEEE Robotics &amp amp amp Automation Magazine

In this work, we present an integrated approach for planning collision-free grasping motions. The proposed Grasp–RRT planner combines the three main tasks needed for grasping an object: building a feasible grasp, solving the inverse kinematics problem and determining a collision-free trajectory that brings the hand to the grasping pose. Therefore, RRT-based algorithms are used to build a tree of reachable and collision-free configurations. During the tree generation, both grasp hypotheses and approach movements toward them are computed. The quality of reachable grasping poses is evaluated using grasp wrench space analysis. We present an extension to a dual arm planner which generates bimanual grasps together with collision-free dual arm grasping motions. The algorithms are evaluated with different setups in simulation and on the humanoid robot ARMAR-III.

2008

We present a novel motion planning algorithm for performing constrained tasks such as opening doors and drawers by robots such as humanoid robots or mobile manipulators. Previous work on constrained manipulation transfers rigid constraints imposed by the target object motion directly into the robot configuration space. This often unnecessarily restricts the allowable robot motion, which can prevent the robot from performing even simple tasks, particularly if the robot has limited reachability or low number of joints. Our method computes "caging grasps" specific to the object and uses efficient search algorithms to produce motion plans that satisfy the task constraints. The major advantages of our technique significantly increase the range of possible motions of the robot by not having to enforce rigid constraints between the end-effector and the target object. We illustrate our approach with experimental results and examples running on two robot platforms.

2008 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 2008

Automatic grasp planning systems are very important for service robots, which compute what forces should be exerted onto the object and how those forces can be applied by robotic hands. In this paper, a highly integrated grasp planning system is introduced. Initial grasp is computed in the grasp simulator GraspIt! combining hand preshapes and automatically generated approach directions. With fixed relative position and orientation between the robotic hand and object as by the initial grasp, all the contact points between the fingers and the object are efficiently found. A search process tries to improve the grasp quality by moving the fingers to its neighbored joint positions, and uses the corresponding contact points to the joint position to evaluate the grasp quality, until local maximum grasp quality is reached. Optimal forces for the found grasp is computed as a linear inequalities matrix problem, which are exerted onto the object using torque based finger impedance control during execution. Experiments on Schunk Anthropomorphic Hand with 13 degrees of freedom show that, using the introduced grasp planning system, the object can be grasped solidly with shift errors of only some millimeters.

ArXiv, 2019

Training a deep network policy for robot manipulation is notoriously costly and time consuming as it depends on collecting a significant amount of real world data. To work well in the real world, the policy needs to see many instances of the task, including various object arrangements in the scene as well as variations in object geometry, texture, material, and environmental illumination. In this paper, we propose a method that learns to perform table-top instance grasping of a wide variety of objects while using no real world grasping data, outperforming the baseline using 2.5D shape by 10%. Our method learns 3D point cloud of object, and use that to train a domain-invariant grasping policy. We formulate the learning process as a two-step procedure: 1) Learning a domain-invariant 3D shape representation of objects from about 76K episodes in simulation and about 530 episodes in the real world, where each episode lasts less than a minute and 2) Learning a critic grasping policy in si...

arXiv (Cornell University), 2022

Robots often have to perform manipulation tasks in close proximity to people (Fig 1). As such, it is desirable to use a robot arm that has limited joint torques so as to not injure the nearby person. Unfortunately, these limited torques then limit the payload capability of the arm. By using contact with the environment, robots can expand their reachable workspace that, otherwise, would be inaccessible due to exceeding actuator torque limits. We adapt our recently developed INSAT algorithm [1] to tackle the problem of torque-limited whole arm manipulation planning through contact. INSAT requires no prior over contact mode sequence and no initial template or seed for trajectory optimization. INSAT achieves this by interleaving graph search to explore the manipulator joint configuration space with incremental trajectory optimizations seeded by neighborhood solutions to find a dynamically feasible trajectory through contact. We demonstrate our results on a variety of manipulators and scenarios in simulation. We also experimentally show our planner exploiting robot-environment contact for the pick and place of a payload using a Kinova Gen3 robot. In comparison to the same trajectory running in free space, we experimentally show that the utilization of bracing contacts reduces the overall torque required to execute the trajectory.

2011

CITATIONS 3 READS 30 3 authors, including: