Abstract: One common robotic task is the rearrangement of physical objects situated in an environment. This typically involves a robot manipulator picking up a target object and placing the target object in some target location, such as a shelf, cabinet or cubby, and requires the skills of picking, placing and generating complex collision-free motions, oftentimes in a cluttered environment. The present disclosure provides collision detection for object rearrangement using a three-dimensional (3D) scene representation.

Abstract: One embodiment of a method for generating simulation data to train a machine learning model includes generating a plurality of simulation environments based on a user input, and for each simulation environment included in the plurality of simulation environments: generating a plurality of tasks for a robot to perform within the simulation environment, performing one or more operations to determine a plurality of robot trajectories for performing the plurality of tasks, and generating simulation data for training a machine learning model by performing one or more operations to simulate the robot moving within the simulation environment according to the plurality of trajectories.

Abstract: One common robotic task is the rearrangement of physical objects situated in an environment. This typically involves a robot manipulator picking up a target object and placing the target object in some target location, such as a shelf, cabinet or cubby, and requires the skills of picking, placing and generating complex collision-free motions, oftentimes in a cluttered environment. The present disclosure provides collision detection for object rearrangement using a three-dimensional (3D) scene representation.

Abstract: Apparatuses, systems, and techniques to generate a motion plan. In at least one embodiment, a motion plan is generated using a neural network based, at least in part, on a demonstration of a task.

Abstract: Apparatuses, systems, and techniques determine a set of grasp poses that would allow a robot to successfully grasp an object that is proximate to at least one additional object. In at least one embodiment, the set of grasp poses is modified based on a determination that at least one of the grasp poses in the set of grasp poses would interfere with at least one additional object that is proximate to the object.

Abstract: Candidate grasping models of a deformable object are applied to generate a simulation of a response of the deformable object to the grasping model. From the simulation, grasp performance metrics for stress, deformation controllability, and instability of the response to the grasping model are obtained, and the grasp performance metrics are correlated with robotic grasp features.

Abstract: Apparatuses, systems, and techniques determine a set of grasp poses that would allow a robot to successfully grasp an object that is proximate to at least one additional object. In at least one embodiment, the set of grasp poses is modified based on a determination that at least one of the grasp poses in the set of grasp poses would interfere with at least one additional object that is proximate to the object.

Abstract: In at least one embodiment, a system determines a set of possible grasp poses that allow a robot to successfully grasp an object by generating a set of potential grasp poses, and then evaluating the performance of each potential grasp pose. In at least one embodiment, the system performs a refinement operation on the grasp poses, and based on an evaluation of the poses, creates an improved set of possible grasps for the object.

Abstract: Candidate grasping models of a deformable object are applied to generate a simulation of a response of the deformable object to the grasping model. From the simulation, grasp performance metrics for stress, deformation controllability, and instability of the response to the grasping model are obtained, and the grasp performance metrics are correlated with robotic grasp features.

Abstract: Apparatuses, systems, and techniques for determining whether collisions will occur in potential paths of an object within a scene. In at least one embodiment, one or more neural networks determine whether collisions will occur in potential paths of an object within a scene based at least in part on point cloud data of the object and the scene.

Abstract: Apparatuses, systems, and techniques determine a set of grasp poses that would allow a robot to successfully grasp an object that is proximate to at least one additional object. In at least one embodiment, the set of grasp poses is modified based on a determination that at least one of the grasp poses in the set of grasp poses would interfere with at least one additional object that is proximate to the object.

Abstract: In at least one embodiment, a system determines a set of possible grasp poses that allow a robot to successfully grasp an object by generating a set of potential grasp poses, and then evaluating the performance of each potential grasp pose. In at least one embodiment, the system performs a refinement operation on the grasp poses, and based on an evaluation of the poses, creates an improved set of possible grasps for the object.