D-wave Systems Inc.

Sanctuary Cognitive Systems Corporation

Kindred Systems Inc.

Geordie Rose

Mohammad H S Amin

Suzanne Gildert

Paul I Bunyk

Peter Love

Jeremy P Hilton

Andrew Joseph Berkley

William G Macready

Miles F H Steininger

David Grant

Alexander Maassen Van Den Brink

Alexandre M Zagoskin

Eugene Dantsker

Byong Hyop Oh

Eric G Ladizinsky

Olivia Norton

Marshall Drew-Brook

Alexander Omelyanchouk

Timothy Duty

Thomas Mahon

Dominic Christoph Walliman

Alexandre Blais

Mani Ranjbar

Firas Hamze

Dmytro Korenkevych

Marcel Franz

Herbert J Martin

Richard G Harris

Randall Black

Michael Coury

Vicky Choi

Thomas F W Mahon

Anatoly Yu Smirnov

Thomas Fw Mahon

Kamran Karimi

Alec Maassen Van Den Brink

Brock Wilson

Miles Fh Steininger

Mohammed Hs Amin

Suzanne Gildbert

Eric Ladizinsky

Mohammed H Amin

Miles F Steininger

Alec M Van Den Brink

Andrew Berkley

Andrew A Berkley

Travis Oh

The present disclosure describes robots and tele-operation systems where a select control paradigm is selected from a plurality of control paradigms based on an identity of an operator, role of an operator, or expected tasks to be performed by the robot. Generic robots can be operated in accordance with any of the plurality of control paradigms, such that any of said generic robots can serve a role or act as an assistant to an operator by selection of an appropriate control paradigm. Control paradigms can be operator specific, or specific to a faction or role which an operator fits in, or specific to a set of tasks to be performed by the robot. The present disclosure also describes feedback mechanisms by which a robot or tele-operator system receive operator feedback and update a control paradigm, to gradually improve the control paradigm over time.

Robots, tele-operation systems, and methods of operating the same

Robot control systems, methods, control modules and computer program products that leverage one or more large language model(s) (LLMs) in order to achieve at least some degree of autonomy are described. Robot control parameters and/or instructions may advantageously be specified in natural language (NL) and communicated with the LLM via a recursive sequence of NL prompts or queries. Corresponding NL responses from the LLM may then be converted into robot control parameters and/or instructions. In this way, an LLM may be leveraged by the robot control system to enhance the autonomy of various operations and/or functions, including without limitation task planning, motion planning, human interaction, and/or reasoning about the environment.

Robot systems, methods, control modules, and computer program products that leverage large language models

Robot control systems, methods, control modules and computer program products that leverage one or more large language model(s) (LLMs) in order to achieve at least some degree of autonomy are described. Robot control parameters and/or instructions may advantageously be specified in natural language (NL) and communicated with the LLM via an NL prompt or query. The LLM module provides a task plan in NL, which can be evaluated for at least one fault or error. If at least one fault or error is identified, the LLM module can be queried to provide a resolution.

Robots, methods, and computer program products for training and operating (semi-) autonomous robots to complete multiple different work objectives are described. A robot accesses a library of reusable work primitives from a catalog of libraries of reusable work primitives, each reusable work primitive corresponding to a respective basic sub-task or sub-action that the robot is operative to autonomously perform. A work objective is analyzed to determine a sequence (i.e., a combination and/or permutation) of reusable work primitives that, when executed by the robot, will complete the work objective. The robot executes the sequence of reusable work primitives to complete the work objective. A robot can be deployed with an appropriate stored library (or access to an appropriate library) of reusable work primitives, based on what the robot is expected to do, or what service category or role the robot will operate in.

Multi-purpose robots and computer program products, and methods for operating the same

In a method of operation of a robot, the robot identifies a set of candidate actions that may be performed by the robot, and collects, for each candidate action of the set of candidate actions, a respective set of ancillary data. The robot transmits a request for instructions to a tele-operation system that is communicatively coupled to the robot. The request for instructions includes each candidate action and each respective set of ancillary data. The robot receives, and executes, the instructions from the tele-operation system. The robot updates a control model, based at least in part on each candidate action, each respective set of ancillary data, and the instructions, to increase a level of autonomy of the robot. The robot may transmit the request for instructions to the tele-operation system in response to determining the robot is unable to select a candidate action to perform in furtherance of an objective.

Systems, devices, and methods for developing robot autonomy

Systems, devices, and methods for training and operating (semi-)autonomous robots to complete multiple different work objectives are described. A robot control system stores a library of reusable work primitives each corresponding to a respective basic sub-task or sub-action that the robot is operative to autonomously perform. A work objective is analyzed to determine a sequence (i.e., a combination and/or permutation) of reusable work primitives that, when executed by the robot, will complete the work objective. The robot executes the sequence of reusable work primitives to complete the work objective. The reusable work primitives may include one or more reusable grasp primitives that enable(s) a robot's end effector to grasp objects. Simulated instances of real physical robots may be trained in simulated environments to develop control instructions that, once uploaded to the real physical robots, enable such real physical robots to autonomously perform reusable work primitives.

Systems, devices, and methods for grasping by multi-purpose robots

Robot control systems, methods, control modules and computer program products that leverage one or more large language model(s) (LLMs) in order to achieve at least some degree of autonomy are described. Robot control parameters, environment details, and/or instructions may advantageously be specified in natural language (NL) and communicated with the LLM via an NL prompt or query. The NL query may include a request for one or more work objectives from the LLM, such as 'What can I do here?', thereby establishing a form of agency by which the robot system may identify activities to perform without operator intervention. The LLM may also be queried to convert each work objective into a task plan providing a sequence of steps that the robot system may execute to complete the work objective. Optionally, the robot system may communicate with an operator to determine whether or not to execute a task plan.

Robot control systems, methods, control modules, and computer program products that leverage one or more large language model(s) (LLMs) in order to achieve at least some degree of autonomy are described. Robot control parameters, environment details, and/or instruction sets may advantageously be specified in natural language (NL) and communicated with the LLM via an NL prompt or query. An NL response from the LLM may then be converted into a task plan. A task plan that successfully completes a first instance of a work objective may be parameterized and re-used to complete a second instance of the work objective. Parameterization of a task plan may include replacing one or more nouns/objects in the NL task plan with variables, while optionally preserving one or more verbs/actions in the NL task plan.

Systems, devices, and methods for multi-purpose robots

Robot control systems, methods, control modules and computer program products that leverage one or more large language model(s) (LLMs) in order to achieve at least some degree of autonomy are described. Robot control parameters and/or instructions may advantageously be specified in natural language (NL) and communicated with the LLM via an NL prompt or query. An NL response from the LLM may then be converted into robot control parameters and/or instructions. In this way, an LLM may be leveraged by the robot control system to enhance the autonomy of various operations and/or functions, including without limitation task planning, motion planning, human interaction, and/or reasoning about the environment.

Systems, devices, and methods for training multi-purpose robots

Growing community of inventors

Vancouver, Canada

Geordie Rose