Abstract: A robot includes a robot body having a first robotic leg, a second robotic leg, and a robotic torso. The first robotic leg includes a first foot, a first lower leg member coupled to the first foot, and a first upper leg member coupled to the first lower leg member. The second robotic leg includes a second foot, a second lower leg member coupled to the second foot, and a second upper leg member coupled to the second lower leg member. The robot includes a mobile base having a platform to which the first and second feet of the robot body are fastened.

Abstract: Systems, methods, and computer program products for automating tasks are described. A multi-step framework enables a gradient towards task automation. An agent performs a task while sensors collect data. The data are used to generate a script that characterizes the discrete actions executed by the agent in the performance of the task. The script is used by a robot teleoperation system to control a robot to perform the task. The robot teleoperation system maps the script into an ordered set of action commands that the robot is operative to auto-complete to enable the robot to semi-autonomously perform the task. The ordered set of action commands is converted into an automation program that may be accessed by an autonomous robot and executed to cause the autonomous robot to autonomously perform the task. In training, simulated instances of the robot may perform simulated instances of the task in simulated environments.

Abstract: Systems, methods, and computer program products for automating tasks are described. A multi-step framework enables a gradient towards task automation. An agent performs a task while sensors collect data. The data are used to generate a script that characterizes the discrete actions executed by the agent in the performance of the task. The script is used by a robot teleoperation system to control a robot to perform the task. The robot teleoperation system maps the script into an ordered set of action commands that the robot is operative to auto-complete to enable the robot to semi-autonomously perform the task. The ordered set of action commands is converted into an automation program that may be accessed by an autonomous robot and executed to cause the autonomous robot to autonomously perform the task. In training, simulated instances of the robot may perform simulated instances of the task in simulated environments.

Abstract: 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.

Abstract: Systems, methods, and computer program products for managing and populating environment models are described. An environment model is accessed which represents an environment, and the environment model is populated with instances of object models. Locations where the instances of object models should be positioned in the environment model are identified, by determining where in the environment model a respective size of each instance when viewed from a vantage point at the environment model matches a size of the object represented by the respective instance when viewed from a corresponding vantage point at the environment.

Abstract: Systems, methods, and computer program products for managing and populating environment models are described. An environment model is accessed which represents an environment, and the environment model is populated with instances of object models. Locations where the instances of object models should be positioned in the environment model are identified, by determining where in the environment model a respective size of each instance when viewed from a vantage point at the environment model matches a size of the object represented by the respective instance when viewed from a corresponding vantage point at the environment.

Abstract: Systems, methods, and computer program products for managing and populating environment models are described. An environment model is accessed which represents an environment, and the environment model is populated with instances of object models. Locations where the instances of object models should be positioned in the environment model are identified, by determining where in the environment model a respective size of each instance when viewed from a vantage point at the environment model matches a size of the object represented by the respective instance when viewed from a corresponding vantage point at the environment.

Abstract: 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.

Abstract: Robots, robot systems, and methods for operating the same based on environment models including haptic data are described. An environment model which includes representations of objects in an environment is accessed, and a robot system is controlled based on the environment model. The environment model incudes haptic data, which provides more effective control of the robot. The environment model is populated based on visual profiles, haptic profiles, and/or other data profiles for objects or features retrieved from respective databases. Identification of objects or features can be based on cross-referencing between visual and haptic profiles, to populate the environment model with data not directly collected by a robot which is populating the model, or data not directly collected from the actual objects or features in the environment.

Abstract: Robots, systems, methods, and computer program products for training and operating (semi-)autonomous robots to complete 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-action that the robot is trained to autonomously perform. A work objective is analyzed to determine a sequence of reusable work primitives that complete the work objective, and the robot executes the sequence to complete the work objective. A robot can be deployed with access to an appropriate library of reusable work primitives, based on expectations for the robot. The robot is trained to perform reusable work primitives in multiple libraries, by generating control instructions which cause the robot to perform each reusable work primitive.

Abstract: Robots, systems, methods, and computer program products for training and operating (semi-)autonomous robots to complete 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-action that the robot is trained to autonomously perform. A work objective is analyzed to determine a sequence of reusable work primitives that complete the work objective, and the robot executes the sequence to complete the work objective. A robot can be deployed with access to an appropriate library of reusable work primitives, based on expectations for the robot. The robot is trained to perform reusable work primitives in multiple libraries, by generating control instructions which cause the robot to perform each reusable work primitive.

Abstract: 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.

Abstract: 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.

Abstract: The present disclosure describes robots, tele-operation systems, methods, and computer program products where a robot is selectively operable in a plurality of control modes. Based on identification of a fault condition (when the robot fails to act in a suitable or sufficient manner), a control mode of the robot can be changed to provide a human operator with more explicit control over the robot. In this way, the fault condition can be resolved by human operator input, and the control modes, AI, or control paradigm for the robot can be trained to perform better in the future.

Abstract: The present disclosure describes robots, tele-operation systems, methods, and computer program products where a robot is selectively operable in a plurality of control modes. Based on identification of a fault condition (when the robot fails to act in a suitable or sufficient manner), a control mode of the robot can be changed to provide a human operator with more explicit control over the robot. In this way, the fault condition can be resolved by human operator input, and the control modes, AI, or control paradigm for the robot can be trained to perform better in the future.

Abstract: The present disclosure describes robots, tele-operation systems, methods, and computer program products where a robot is selectively operable in a plurality of control modes. Based on identification of a fault condition (when the robot fails to act in a suitable or sufficient manner), a control mode of the robot can be changed to provide a human operator with more explicit control over the robot. In this way, the fault condition can be resolved by human operator input, and the control modes, AI, or control paradigm for the robot can be trained to perform better in the future.

Abstract: Robots, robot systems, and methods for operating the same based on environment models including haptic data are described. An environment model which includes representations of objects in an environment is accessed, and a robot system is controlled based on the environment model. The environment model incudes haptic data, which provides more effective control of the robot. The environment model is populated based on visual profiles, haptic profiles, and/or other data profiles for objects or features retrieved from respective databases. Identification of objects or features can be based on cross-referencing between visual and haptic profiles, to populate the environment model with data not directly collected by a robot which is populating the model, or data not directly collected from the actual objects or features in the environment.

Abstract: 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.

Abstract: The present disclosure relates to utilizing idle processing resources of a robot to reduce future burden on such processing resources. In particular, idle processing resources are utilized to identify future scenarios, and generate reactions to such future scenarios. The generated reactions are stored, and quickly retrieved as needed if corresponding identified future scenarios occur.

Abstract: The present disclosure relates to utilizing idle processing resources of a robot to reduce future burden on such processing resources. In particular, idle processing resources are utilized to identify future scenarios, and generate reactions to such future scenarios. The generated reactions are stored, and quickly retrieved as needed if corresponding identified future scenarios occur.