Dexterity, Inc.

Ben Varkey Benjamin Pottayil

Kevin Jose Chavez

Rohit Arka Pidaparthi

William Arthur Clary

Shitij Kumar

Jonathan Kuck

Neeraja Abhyankar

Zhouwen Sun

Samir Menon

Harry Zhe Su

Talbot Morris-Downing

Roger Lam Hau

Salvador Perez

Robert Moreno

Jeesu Baek

Adam Kell

Andrew Dupree

William Hyslop

Arth Beladiya

A robotic system is disclosed. The system includes a communication interface configured to receive, from one or more sensors deployed in a workspace, sensor data indicative of a current state of the workspace, the workspace comprising a pallet or other receptacle and a plurality of items stacked on or in the receptacle. The system includes one or more processors that use a geometric model based at least in part on past item placements in combination with the sensor data to estimate a state of the pallet or other receptacle and one or more items stacked on or in the pallet or other receptacle, and use the estimated state to generate or update a plan to control a robotic arm to place a next item on or in, or remove a next item from, the pallet or other receptacle in a manner that avoids having the next item collide with any other item stacked on or in the pallet or other receptacle.

Collision avoidance based on vision data, geometric data and physics engine evaluation

A robotic system is disclosed. The system includes a memory that stores a machine learning-based model to provide a scoring function value for a candidate item placement on a pallet on which are plurality of items are to be stacked given a current state value of the pallet and a set of zero or more items placed previously. The system includes one or more processors that use the model to determine a corresponding score for each of a plurality of candidate placements for a next item to be placed and the current state value associated with the current state of the pallet and a set of zero or more items placed previously, select a selected placement based at least in part on the respective scores, control a robotic arm to place the next item according to the selected placement.

Workflow for using learning based approach for placing boxes on pallets

A robotic system is disclosed. The system includes a communication interface, and one or more processors coupled to the communication interface and configured to: generate based at least in part on the received data a plan to stack the items on or in the destination location. For each item, the generating the plan includes determining the destination location based at least in part on a characteristic associated with the item, and at least one of (i) a characteristic of a platform or receptacle on which one or more items are to be stacked, and (ii) an existing stack of one or more items on the platform or receptacle. The destination location is determined from among a plurality of zones in which platforms or receptacles are disposed, and each of the plurality of zones are within a range of a robotic arm. A robotic arm is controlled to implement the plan.

Multi-pallet mixed-case robotic palletizer

A robotic system is disclosed. The system includes a communication interface that receives, from one or more sensors deployed in a workspace, sensor data indicative of a current state of the workspace. The system includes one or more processors that use the sensor data to estimate a state of one or both of the pallet or other receptacle and the set of zero or more items stacked on or in the receptacle, and use the estimated state to generate or update a plan to control a robotic arm to place a next set of items on or in, or remove the next set of items from, the pallet or other receptacle, the plan comprising an ordered sequence of item placements or removals. The plan is generated or updated based at least in part by performing a bounded tree search in which a subset of possible ordered sequences is explored.

Workflow for using tree search-based approach for placing boxes on pallet with limited knowledge of future sequence

A set of one or more potentially graspable features for one or more objects present in a workspace area are determined based on visual data received from a plurality of cameras. For each of at least a subset of the one or more potentially graspable features one or more corresponding grasp strategies are determined to grasp the feature with a robotic arm and end effector. A score associated with a probability of a successful grasp of a corresponding feature is determined with respect to each of a least a subset of said grasp strategies. A first feature of the one or more potentially graspable features is selected to be grasped using a selected grasp strategy based at least in part on a corresponding score associated with the selected grasp strategy with respect to the first feature. The robotic arm and the end effector are controlled to attempt to grasp the first feature using the selected grasp strategy.

Autonomous unknown object pick and place

Techniques are disclosed to use robotic system simulation to control a robotic system. In various embodiments, a communication indicating an action to be performed by a robotic element is received from a robotic control system. Performance of the action by the robotic element is simulated. A state tracking data is updated to reflect a virtual change to one or more state variables as a result of simulated performance of the action. Successful completion of the action by the robotic element is reported to the robotic control system.

Robotic system simulation engine

A robotic system is disclosed. The system includes a memory that stores for each of a plurality of items a set of attribute values. The system includes a processor(s) that uses the attribute values to simulate the placement of items, including by determining, iteratively, for each next item a placement location at which to place the item on a simulated stack of items on the pallet, using the attribute values and a geometric model of where items have been simulated to have been placed to estimate a state of the stack after each of a subset of simulated placements, and using the estimated state to inform a next placement decision. The steps of determining for each next item a placement location and estimating the state of the stack until all of at least a subset of the plurality of items have been simulated as having been placed on the stack.

Simulated box placement for algorithm evaluation and refinement

A robotic system is disclosed. The system includes a communication interface that receives, from a sensor(s) deployed in a workspace, sensor data indicative of a current state of the workspace, the workspace comprising a pallet or other receptacle and a plurality of items stacked on or in the receptacle. The system includes one or more processors that control a robotic arm to place a first set of items on or in, or remove the first set of items from, the pallet or other receptacle, update a geometric model based on the first set of items placed on or in a receptacle, use the geometric model in combination with the sensor data to estimate a stack of one or more items on or in the receptacle, and use the estimated state to generate or update a plan to control the robotic arm to place a second set of items.

Growing community of inventors

Santa Clara, CA, United States of America

Ben Varkey Benjamin Pottayil