Abstract: Systems, apparatus, and methods are described for robotic learning and execution of skills. A robotic apparatus can include a memory, a processor, sensors, and one or more movable components (e.g., a manipulating element and/or a transport element). The processor is operatively coupled to the memory, the movable elements, and the sensors, and configured to obtain information of an environment, including one or more objects located within the environment. In some embodiments, the processor is configured to learn skills through demonstration, exploration, user inputs, etc. In some embodiments, the processor is configured to execute skills and/or arbitrate between different behaviors and/or actions. In some embodiments, the processor is configured to learn an environmental constraint. In some embodiments, the processor is configured to learn using a general model of a skill.

Abstract: Systems, apparatus, and methods are described for robotic learning and execution of skills. A robotic apparatus can include a memory, a processor, sensors, and one or more movable components (e.g., a manipulating element and/or a transport element). The processor can be operatively coupled to the memory, the movable elements, and the sensors, and configured to obtain information of an environment, including one or more objects located within the environment. In some embodiments, the processor can be configured to learn skills through demonstration, exploration, user inputs, etc. In some embodiments, the processor can be configured to execute skills and/or arbitrate between different behaviors and/or actions. In some embodiments, the processor can be configured to learn an environmental constraint. In some embodiments, the processor can be configured to learn using a general model of a skill.

Abstract: Systems, apparatus, and methods are described for robotic learning and execution of skills. A robotic apparatus can include a memory, a processor, sensors, and one or more movable components (e.g., a manipulating element and/or a transport element). The processor can be operatively coupled to the memory, the movable elements, and the sensors, and configured to obtain information of an environment, including one or more objects located within the environment. In some embodiments, the processor can be configured to learn skills through demonstration, exploration, user inputs, etc. In some embodiments, the processor can be configured to execute skills and/or arbitrate between different behaviors and/or actions. In some embodiments, the processor can be configured to learn an environmental constraint. In some embodiments, the processor can be configured to learn using a general model of a skill.

Abstract: Systems, apparatus, and methods are described for robotic learning and execution of skills. A robotic apparatus can include a memory, a processor, sensors, and one or more movable components (e.g., a manipulating element and/or a transport element). The processor can be operatively coupled to the memory, the movable elements, and the sensors, and configured to obtain information of an environment, including one or more objects located within the environment. In some embodiments, the processor can be configured to learn skills through demonstration, exploration, user inputs, etc. In some embodiments, the processor can be configured to execute skills and/or arbitrate between different behaviors and/or actions. In some embodiments, the processor can be configured to learn an environmental constraint. In some embodiments, the processor can be configured to learn using a general model of a skill.

Abstract: Systems, apparatus, and methods are described for robotic learning and execution of skills. A robotic apparatus can include a memory, a processor, sensors, and one or more movable components (e.g., a manipulating element and/or a transport element). The processor can be operatively coupled to the memory, the movable elements, and the sensors, and configured to obtain information of an environment, including one or more objects located within the environment. In some embodiments, the processor can be configured to learn skills through demonstration, exploration, user inputs, etc. In some embodiments, the processor can be configured to execute skills and/or arbitrate between different behaviors and/or actions. In some embodiments, the processor can be configured to learn an environmental constraint. In some embodiments, the processor can be configured to learn using a general model of a skill.

Abstract: Systems, apparatus, and methods are described for robotic learning and execution of skills. A robotic apparatus can include a memory, a processor, sensors, and one or more movable components (e.g., a manipulating element and/or a transport element). The processor can be operatively coupled to the memory, the movable elements, and the sensors, and configured to obtain information of an environment, including one or more objects located within the environment. In some embodiments, the processor can be configured to learn skills through demonstration, exploration, user inputs, etc. In some embodiments, the processor can be configured to execute skills and/or arbitrate between different behaviors and/or actions. In some embodiments, the processor can be configured to learn an environmental constraint. In some embodiments, the processor can be configured to learn using a general model of a skill.

Abstract: A method of rendering a machine state, a method of generating and rendering a work-piece cut path, and a CNC machines system are disclosed.

Abstract: A method of rendering a machine state, a method of generating and rendering a work-piece cut path, and a CNC machines system are disclosed.

Abstract: A method of rendering a machine state, a method of generating and rendering a work-piece cut path, and a CNC machines system are disclosed.

Abstract: A method of rendering a machine state, a method of generating and rendering a work-piece cut path, and a CNC machines system are disclosed.

Abstract: A family of reconfigurable asynchronous logic elements that interact with their nearest neighbors permits reconfigurable implementation of circuits that are asynchronous at the bit level. A reconfigurable asynchronous logic cell comprises a set of one-bit buffers for communication with at least one neighboring cell, each buffer capable of having several states and configured for receiving input state tokens from neighboring cells and for transferring output state tokens to neighboring cells, and a one-bit processor configured to perform a logic operation utilizing received tokens as inputs and to produce an output token reflecting the result of the logic operation, wherein the logic operation and the functional configuration of the buffers are reconfigurably programmable. A reconfigurable logic circuit comprises a plurality of reconfigurable logic cells that compute by locally passing state tokens and are reconfigured by the directed shifting of programming instructions through neighboring logic cells.

Abstract: A family of reconfigurable asynchronous logic elements that interact with their nearest neighbors permits reconfigurable implementation of circuits that are asynchronous at the bit level. A reconfigurable asynchronous logic cell comprises a set of one-bit buffers for communication with at least one neighboring cell, each buffer capable of having several states and configured for receiving input state tokens from neighboring cells and for transferring output state tokens to neighboring cells, and a one-bit processor configured to perform a logic operation utilizing received tokens as inputs and to produce an output token reflecting the result of the logic operation, wherein the logic operation and the functional configuration of the buffers are reconfigurably programmable. A reconfigurable logic circuit comprises a plurality of reconfigurable logic cells that compute by locally passing state tokens and are reconfigured by the directed shifting of programming instructions through neighboring logic cells.

Abstract: A family of reconfigurable asynchronous logic elements that interact with their nearest neighbors permits reconfigurable implementation of circuits that are asynchronous at the bit level, rather than at the level of functional blocks. These elements pass information by means of tokens. Each cell is self-timed, and cells that are configured as interconnect perform at propagation delay speeds, so no hardware non-local connections are needed. A reconfigurable asynchronous logic element comprises a set of edges for communication with at least one neighboring cell, each edge having an input for receiving tokens from neighboring cells and an output for transferring tokens to at least one neighboring cell, circuitry configured to perform a logic operation utilizing received tokens as inputs and to produce an output token reflecting the result of the logic operation, and circuitry.

Abstract: A family of reconfigurable asynchronous logic elements that interact with their nearest neighbors permits reconfigurable implementation of circuits that are asynchronous at the bit level, rather than at the level of functional blocks. These elements pass information by means of tokens. Each cell is self-timed, and cells that are configured as interconnect perform at propagation delay speeds, so no hardware non-local connections are needed. A reconfigurable asynchronous logic element comprises a set of edges for communication with at least one neighboring cell, each edge having an input for receiving tokens from neighboring cells and an output for transferring tokens to at least one neighboring cell, circuitry configured to perform a logic operation utilizing received tokens as inputs and to produce an output token reflecting the result of the logic operation, and circuitry.