Robotic arms or manipulators can be utilized to grasp inventory items within an inventory system. Information can be obtained about constraints relative to relevant elements of a process of transferring the item from place to place. Examples of such elements may include a grasping location from which an item is to be grasped, a receiving location in which a grasped item is to be placed, or a space between the grasping location and the receiving location. The information about the constraints can be used to select from multiple possible grasping options, such as by eliminating options that conflict with the constraints or preferring options that outperform others given the constraints.

A controller is provided for performing dynamic grasping of a target object using visual sensory inputs. The controller includes a robotic interface connected to a robotic arm including links connected by joints having actuators and encoders, and a gripper of the end-effector of the robotic arm configured to grasp the target object in response to robot control signals, and a vision sensor configured to continuously provide visual observations for tracking poses of the target object in a workspace and compute grasp poses, wherein the vision sensor is mounted on a distal end of the robotic arm adjacent to the gripper. The controller trains the Eye-on-Hand reinforcement learner policy, tracks the poses of the target object, and generates robot control signals to follow the target object while keeping it in the field of view of the vision sensor and grasp the target object in the workspace.

A method of using a polyarticulated system associated with a vision system for automatically picking up an article situated in a zone suitable for receiving at least one article, the polyarticulated system including at least one pick-up member suitable for taking hold of an article via at least one specific zone of the article. In accordance with the invention, the method includes at least the steps of: taking an image of the article-receiving zone; processing the information resulting from the 3D image and identifying all of the specific zones that are present on the articles to be taken hold of, and that are compatible with the pick-up member(s); locating the identified compatible specific zone(s); choosing one of the located compatible specific zones and automatically defining a pick path; and taking hold of the corresponding article along the defined path.

Robotic arms or manipulators can be utilized to grasp inventory items within an inventory system. Information about an item to be grasped can be detected and/or accessed from one or more databases to determine a grasping strategy for grasping the item with a robotic arm or manipulator. For example, one or more accessed databases can contain information about the item, characteristics of the item, and/or similar items, such as information indicating grasping strategies that have been successful or unsuccessful for such items in the past.

Robotic arms may be utilized to grasp inventory items within an inventory system. Information about an inventory item to be grasped can be detected and used to determine a grasping strategy in conjunction with information from a database. Instructions for grasping an inventory item can be generated based on the detected information and the database.

An intelligent system, such as an autonomous robot agent, includes systems and methods to learn various aspects about a task in response to instructions received from a human instructor, to apply the instructed knowledge immediately during task performance following the instruction, and to instruct other intelligent systems about the knowledge for performing the task. The learning is accomplished free of training the intelligent system. The instructions from the human instructor may be provided in a natural language format and may include deictic references. The instructions may be received while the intelligent system is online, and may be provided to the intelligent system in one shot, e.g., in a single encounter or transaction with the human instructor.

Technologies are generally described for en route food product preparation. Food product preparation process steps and timing may be determined based on travel information (e.g., starting point, intermediate waypoints, delivery destination, routes, etc.), as well as, food item and food product information. Instructions for robotic devices arranged modularly in a container or truck to execute steps of the food product preparation process and their timing may be transmitted to a controller managing the operations of the robotic devices. Instructions may be updated en route based on changing travel or other conditions.