Abstract: A first input is received from a plurality of sensors. A first state including a first location based on the first input is determined. The first state is associated with a first probability. A second input is received from the plurality of sensors. A second state including a second location is determined based on the second input associated with a second probability. It is determined that the second state corresponds to an actual state based on a transition model and the second probability. The transition model associates the first state with the second state and indicates a likelihood of a transition from the first state to the second state. A rule to change a state of at least one network connected device is triggered based on the second state.

Abstract: A first input is received from at least one of a plurality of sensors. Based on the first input, a first probability associated with a first state is determined. A second input is received from at least one of the plurality of sensors. Based on the second input, a second probability associated with a second state is determined. The second state is based on a transition model and the second probability. The transition model associates the first state with the second state and indicates a likelihood of a transition from the first state to the second state. A rule is triggered to change a state of at least one network connected device based on the second state.

Abstract: Provided herein are methods and systems for implementing three-dimensional perception in an autonomous robotic system comprising an end-to-end neural network architecture that directly consumes large-scale raw sparse point cloud data and performs such tasks as object localization, boundary estimation, object classification, and segmentation of individual shapes or fused complete point cloud shapes.

Abstract: A network connected controllable device is automatically controlled. A plurality of inputs that indicate states of a network connected device environment associated with a property of the network connected controllable device to be automatically controlled is received. A plurality of output candidates is evaluated according to an optimization function. The optimization function depends on the plurality of inputs and one or more parameters and the output candidates are associated with candidate settings of the property of the network connected controllable device to be automatically controlled. An output that optimizes the optimization function is selected among the plurality of output candidates.

Abstract: A first input is received from at least one of a plurality of sensors. Based on the first input, a first probability associated with a first state is determined. A second input is received from at least one of the plurality of sensors. Based on the second input, a second probability associated with a second state is determined. The second state is based on a transition model and the second probability. The transition model associates the first state with the second state and indicates a likelihood of a transition from the first state to the second state. A rule is triggered to change a state of at least one network connected device based on the second state.

Abstract: Provided herein are methods and systems for implementing three-dimensional perception in an autonomous robotic system comprising an end-to-end neural network architecture that directly consumes large-scale raw sparse point cloud data and performs such tasks as object localization, boundary estimation, object classification, and segmentation of individual shapes or fused complete point cloud shapes.

Abstract: A first input is received from a plurality of sensors. A first state including a first location based on the first input is determined. The first state is associated with a first probability. A second input is received from the plurality of sensors. A second state including a second location is determined based on the second input associated with a second probability. It is determined that the second state corresponds to an actual state based on a transition model and the second probability. The transition model associates the first state with the second state and indicates a likelihood of a transition from the first state to the second state. A rule to change a state of at least one network connected device is triggered based on the second state.

Abstract: A first input is received from a plurality of sensors. A first state including a first location based on the first input is determined. The first state is associated with a first probability. A second input is received from the plurality of sensors. A second state including a second location is determined based on the second input associated with a second probability. It is determined that the second state corresponds to an actual state based on a transition model and the second probability. The transition model associates the first state with the second state and indicates a likelihood of a transition from the first state to the second state. A rule to change a state of at least one network connected device is triggered based on the second state.