A computing system including a processing circuit in communication with a robot and a camera having a field of view. The processing circuit obtains image information based on the objects in the field of view and a loading environment, the loading environment which includes loading areas, an object queue, and a buffer zone. The computing system is configured to use the obtained image information in motion planning operations for the retrieval and placement of objects from the object queue into the loading environment. Pallets provided within the loading environment (i.e., within the loading areas) are dedicated to receiving objects having corresponding object type identifiers. The computer system further uses the image information to determine the fill status of pallets existing within the loading environment, and whether new pallets need to be brought into the loading environment and/or swapped out with existing pallets to account for future planning and placement operations.

An object processing system is disclosed that includes a plurality of track sections, and a plurality of remotely actuatable carriers for controlled movement along at least portions of the plurality of track sections, wherein each of the remotely controllable carriers is adapted to support and transport an object processing bin.

Disclosed is a deep reinforcement learning apparatus and method for a pick-and-place system. According to the present disclosure, a simulation learning framework is configured to apply reinforcement learning to make pick-and-place decisions using a robot operating system (ROS) in a real-time environment, thereby generating stable path motion that meets various hardware and real-time constraints.

A system and method for estimating aspects of target objects and/or associated task implementations is disclosed.

The foodstuff assembly system can include: a robot arm, a frame, a set of foodstuff bins, a sensor suite, a set of food utensils, and a computing system. The system can optionally include: a container management system, a human machine interface (HMI). However, the foodstuff assembly system 100 can additionally or alternatively include any other suitable set of components. The system functions to enable picking of foodstuff from a set of foodstuff bins and placement into a container (such as a bowl, tray, or other foodstuff receptacle). Additionally or alternatively, the system can function to facilitate transferal of bulk material (e.g., bulk foodstuff) into containers, such as containers moving along a conveyor line.

A component installation system includes an engagement clamp having a circular shape coupled to a robotic arm, being configured to engage an annular component. The component installation system further includes an actuator coupled to the engagement clamp and operable to cause the engagement clamp to engage and disengage the annular component, and a heating element coupled to the engagement clamp and arranged to heat the annular component engaged within the engagement clamp. The component installation system further includes a controller in communication with the robotic arm, the actuator and the heating element.

In the present disclosure, methods, systems, and apparatuses for in-process assembly error correction are described. In various embodiments, a target arrangement of parts of an assembly may be obtained, with the target arrangement including a first target position of a first part, a second target position of a second part, and a third target position of a third part. The first part and the second part may be robotically joined based on the first target position and the second target position to obtain a first subassembly of the assembly, with the first subassembly having a first physical arrangement that includes the physical arrangement of the first and second parts after joining. The first physical arrangement may be fitted to the target arrangement to obtain a fitted first physical arrangement. The first subassembly and the third part may be robotically joined based on the fitted first physical arrangement.

In an embodiment, a method includes determining a given material to manipulate to achieve a goal state. The goal state can be one or more deformable or granular materials in a particular arrangement. The method further includes, for the given material, determining, a respective outcome for each of a plurality of candidate actions to manipulate the given material. The determining can be performed with a physics-based model, in one embodiment. The method further can include determining a given action of the candidate actions, where the outcome of the given action reaching the goal state is within at least one tolerance. The method further includes, based on a selected action of the given actions, generating a first motion plan for the selected action.

A conveyance robot system according to the present disclosure includes an intrusion detection sensor that detects an intrusion of an object into the arm opening, and a distance sensor that measures a clearance distance indicating a distance between an arm entry/exit surface and a shelf, the arm entry/exit surface being a surface of the conveyance robot in which the arm opening is provided from among surfaces of the conveyance robot 1 constituting the safety cover, and the object being stored in the shelf. The distance sensor is disposed at a fixed height of the shelf in a horizontal direction and at a height of the shelf corresponding to a part to be measured.

An alternative to additive manufacturing is disclosed, introducing an end-to-end workflow in which discrete building blocks are reversibly joined to produce assemblies called digital materials. Described is the design of the bulk-material building blocks and the devices that are assembled from them. Detailed is the design and implementation of an automated assembler, which takes advantage of the digital material structure to avoid positioning errors within a large tolerance. To generate assembly sequences, a novel CAD/CAM workflow is described for designing, simulating, and assembling digital materials. The structures assembled using this process have been evaluated, showing that the joints perform well under varying conditions and that the assembled structures are functionally precise.