Abstract: An apparatus, including: an interface configured to receive a target end-effector pose of a cobot; processing circuitry configured to: generate in a generic robot model a joint trajectory based on the target end-effector pose; employ a trained neural network model to map the joint trajectory generated in the generic robot model into a cobot model; and generate a movement instruction to control a movement of the cobot based on the joint trajectory mapped to the cobot model, wherein the generic robot model has a number of degrees of freedom that is equal to or greater than that of the cobot model.

Abstract: A system for human-cobot (collaborative robot) ergonomic interaction, including: a communication interface operable to receive sensor data related to human motion; ergonomic assessment processor circuitry operable to evaluate the sensor data to generate a strain score for at least one human joint, wherein the strain score represents a strain level of the at least one human joint based on an integration of motion of the at least one human joint over a period of time; human intent prediction processor circuitry operable to interpret the sensor data to predict an object the human intends to grasp, and to select a destination container for the predicted object; and cobot motion processor circuitry operable to determine a position or orientation for the cobot to place the selected destination container based on the predicted object and the strain score.

Abstract: Techniques are disclosed for a trajectory planning of robots, such as collaborative robots (cobots). A controller of a robot may include a path planner, a trajectory generator, and a trajectory controller. The path planner may determine a plurality of waypoints defining a path between an initial pose of the robot and a goal pose of the robot. The trajectory generator may determine a trajectory between the initial pose and the goal pose based on the waypoints and one or more trajectory criterion. The trajectory controller may generate a control signal to control the robot based on the determined trajectory.

Abstract: A method for calculating a time to contact of an autonomous vehicle, the method comprising: obtaining a plurality of event data an image, wherein the event data is associated with a pixel associated with a change in light intensity; determining a reference signal frequency associated with a transmitted light; identifying a select event data from the plurality of event data, wherein the light frequency associated with the select event data is substantially the same as the reference signal frequency; determining an object based on the select event data, wherein the object is fully enclosed by a bounding box comprising coordinates of a rectangular border; calculating a distance between a set of coordinates of the bounding box closest to the autonomous vehicle and the autonomous vehicle; and calculating the time to contact between the set of coordinates of the bounding box and the autonomous vehicle.

Abstract: A component of a robotic system, including: processor circuitry; and a non-transitory computer-readable storage medium including instructions that, when executed by the processor circuitry, cause the processor circuitry to: train a neuro-capability map plugin, which is a continuous or semi-continuous resolution neural network component encoded with kinematic capability attributes with respect to an action to be performed by a robot in a workspace; and publish the neuro-capability map plugin to a robotic skills repository where it is obtainable by robotic controller circuitry for embedding within a neural network usable perform one or more inferences to control the robot to perform the action.

Abstract: A system can map occupancy of objects. The system may generate an occupancy representation of an object based on a point cloud of the object. The system may generate first geometric entities based on the point cloud. Each first geometric entity contains one or more points in the point cloud. The system may also generate one or more second geometric entities, each of which contains one or more first geometric entities. The occupancy representation of the object includes the one or more second geometric entities and the plurality of first geometric entities. The occupancy representation may have a hierarchical structure where the first geometric entities may be on a lower level than the one or more second geometric entities. The system can also detect collision of the object with another object by using the occupancy representation of the object and an occupancy representation of the other object.

Abstract: Techniques are disclosed to facilitate, in autonomous vehicles, the robust detection and classification of objects in a scene using a static sensors in conjunction with event-based sensors. A trained system architecture may be implemented, and the fusion of both sensors thus allows for the consideration of scenes with overexposure, scenes with underexposure, as well as scenes in which there is no movement. In doing so, the autonomous vehicle may detect and classify objects in conditions in which each sensor, if operating separately, would not otherwise be able to classify (or classify with high uncertainty) due to the sensing environment.

Abstract: A robot configured to be operable within a multi-robot system. The robot includes an input configured to receive global coordinate state information of the robot and of any neighboring robots or obstacles; and processing circuitry configured to: transform the global coordinate state information into a relative coordinate system that is with respect to the robot and is based on a type of desired formation of the robot and any neighboring robots or obstacles around a point; generate a reference formation algorithm which is based on the desired formation; and controlling, based on the reference formation algorithm and tracking errors between the desired formation and a current state of the robot, a trajectory of the robot to converge towards the desired formation while avoiding collisions with any neighboring robots or obstacles.

Abstract: Techniques are disclosed for a decentralized path and motion planning of autonomous agents within an environment. The planning may include determining if an active neighboring autonomous agent is present and selectively controlling the autonomous agent to operation in in an independent path planning operation mode and in a coordinating path planning operation mode, based on the detection of the neighboring agent(s).

Abstract: Methods and apparatus for dynamically routing robots based on exploratory on-board mapping are disclosed. A control system of a robot includes an image manager to command a depth camera to capture depth images of an environment. The depth camera has a field of view. The control system further includes a map generator to generate a map of the environment based on the depth images. The map includes a representation of unoccupied space within the environment, and a path extending through the unoccupied space from a reference location of the robot to a target location of the robot. The control system further includes a field of view evaluator to determine whether the field of view associated with the reference location satisfies a threshold. The control system further includes a route generator to generate, in response to the field of view associated with the reference location satisfying the threshold, a route to be followed by the robot within the environment.

Abstract: Methods and apparatus to facilitate autonomous navigation of robotic devices. An example autonomous robot includes a region model analyzer to: analyze a first image of an environment based on a first neural network model, the first image captured by an image sensor of the robot when the robot is in a first region of the environment; and analyze a second image of the environment based on a second neural network model, the second image captured by the image sensor when the robot is in a second region of the environment, the second neural network associated with the second region. The example robot further includes a movement controller to: autonomously control movement of the robot within the first region toward the second region based on the analysis of the first image; and autonomously control movement of the robot within the second region based on the analysis of the second image.

Abstract: Systems and techniques for an architecture for contextual memories in map representation for 3D reconstruction and navigation are described herein. In an example, a system for contextual memory mapping is adapted to receive a data set of physical world sensor readings. The system may be further adapted to generate voxel data from the data set, the voxel data includes voxel coordinates and a physical world occupancy indicator. The system may be further adapted to select a block of addresses in the memory to store the voxel data. The system may be further adapted to generate a hash map to map voxel coordinates to memory locations in the block of addresses, the voxel coordinates having a contextual relationship that is maintained by the hash map. The system may be further adapted to store the voxel data at memory addresses based on the hash map.

Abstract: A human-robot collaboration system, including at least one processor; and a non-transitory computer-readable storage medium including instructions that, when executed by the at least one processor, cause the at least one processor to: predict a human atomic action based on a probability density function of possible human atomic actions for performing a predefined task; and plan a motion of the robot based on the predicted human atomic action.

Abstract: An apparatus of an autonomous device comprises one or more state estimators to estimate one or more states of the autonomous device, wherein the one or more state estimators are to generate one or more derivatives of translational measurements, orientation measurements, reference translational values, and reference orientation values, and one or more controllers to receive an output from the one or more state estimators to provide control signals to control the autonomous device. The one or more state estimators include a hardware differentiator to generate the one or more derivatives.

Abstract: Techniques are disclosed to facilitate cooperative mapping for safe and efficient trajectory planning and collision avoidance by allowing nearby agents to share contextual information. The described techniques also function to extend the mapping range of a single agent by leveraging observations made by multiple agents. Furthermore, the techniques as described herein function to reduce uncertainty in trajectory planning by allowing agents to “see” behind occlusions, thus taking advantage of observations made by neighboring agents from different points of view. An efficient hardware implementation of the system is also presented that leverages the methodologies as discussed herein.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to facilitate knowledge sharing among neural networks. An example apparatus includes a trainer to train, at a first computing system, a first Bayesian Neural Network (BNN) on a first subset of training data to generate a first weight distribution, and train, at a second computing system, a second BNN on a second subset of the training data to generate a second weight distribution, the second subset of the training data different from the first subset of training data. The example apparatus includes a knowledge sharing controller to generate a third BNN based on the first weight distribution and the second weight distribution.

Abstract: Methods and apparatus for reducing energy consumed by drones during flight are disclosed. A drone includes a housing, a motor, receiver circuitry carried by the housing, and a route manager. The receiver circuitry is to receive airborne drone-generated wind data from an airborne drone located in an area within which a segment of a flight of the drone is to occur. The airborne drone-generated wind data is to be determined by an inertial measurement unit of the airborne drone. The route manager is to generate a route for the flight of the drone based on wind data, the wind data including the airborne drone-generated wind data. The route is to be followed by the drone during the flight. The route manager is to select at least one portion of the route to cause the drone to be at least partially propelled by wind to reduce energy consumed by the drone during the flight.

Abstract: A drone controller comprises processing circuitry to, at a first time when the drone is in flight, to determine a drone state comprising a position and a velocity of the drone and determine a relative obstacle state comprising a relative position and a relative velocity of the drone with respect to an obstacle. The processor then determines a reaction to avoid the obstacle based on the relative obstacle state and applies a signal related to the reaction to one or more actuator control inputs of the drone that modifies a drone path existing at the first time to avoid the obstacle.

Abstract: System and techniques for drone obstacle avoidance using real-time wind estimation are described herein. A wind metric is measures at a first drone and communicated to a second drone. In response to receiving the wind metric, a flight plan of the second drone is modified based on the wind metric.

Abstract: Systems and methods for detecting rogue vehicles within a plurality of vehicles connected via a vehicular ad-hoc network (VANET) are provided. Sensors on each VANET vehicle provide host vehicle data and data associated with other nearby vehicles. Each VANET vehicle multicasts information that includes location, velocity, and preferred future travel path to the other VANET vehicles. Using data from sensors and data received from other VANET vehicles the host vehicle generates a dynamic set of safe vehicle operating behaviors. Nearby vehicles that do not comply with the determined safe vehicle operating behaviors or perform illegal/unsafe acts are identified as rogue vehicles. Data associated with identified rogue vehicles is transmitted to all VANET vehicles.