Abstract: Embodiments of adaptive multi-modal event detection are disclosed herein. In one example, sensor data captured by multiple sensors is received, and an inconsistency among the sensors is detected based on performing event detection on the sensor data. An external environment of the sensors is detected based on the sensor data, and one or more configuration parameter(s) for event detection are adjusted based on the external environment of the sensors. Event detection is then performed on the sensor data based on the adjusted configuration parameter(s).

Abstract: Disclosed herein are systems, devices, and methods for improving the safety of a robot. The safety system may determine a safety envelope of a robot based on a planned movement of the robot and based on state information about a load carried by a robot. The state information may include a dynamic status of the load. The safety system may also determine a safety risk based on a detected object with respect to the safety envelope. The safety system may also generate a mitigating action to the planned movement if the safety risk exceeds a threshold value.

Abstract: Systems and techniques for controlling a mobile robot. In an example, the system may include a processor and memory, including instructions, which when executed by the processor, cause the processor to determine a state of the mobile robot. The state of the mobile robot may be determined using at least one of: data provided by the mobile robot or data captured by one or more sensors proximate to or attached to the mobile robot. The system may determine a state of an object proximate to the mobile robot using the data captured by the one or more sensors, and identify information relating to one or more available stopping points. The system may identify a condition of the mobile robot that requires the mobile robot to stop and issue a command to the mobile robot to navigate to a particular one of the one or more available stopping points.

Abstract: Systems and techniques for reliable real-time deployment of robot safety updates are described herein. Condition data may be collected for an environment in which a robot is operating. The robot may be classified with a condition type. Condition type data selected from the condition data may be analyzed based on the condition type to calculate a safety risk level for the robot. A microservice may be identified to provide a robot safety rule for the robot based on the safety risk level. The microservice may be identified using a safety prediction model generated from the condition type data.

Abstract: Disclosed herein are systems, devices, and methods of a safety system for analyzing and improving the safety of collaborative environments in which a robot may interact a human. The safety system may determine a monitored attribute of a person within an operating environment of a robot, where the monitored attribute may be based on received sensor information about the person in the operating environment. In addition, the safety system may determine a risk score for the person based on the monitored attribute. The risk score may be defined by (1) a collision probability that the person will cause an interference during a planned operation of the robot and (2) a severity level associated with the interference. The safety system may also generate a mitigating instruction for the robot if the risk score exceeds a threshold level.

Abstract: Disclosed herein are systems, devices, and methods of a sanitation system for intelligently adapting the cleaning operations of a sanitation robot. The sanitation system detects a plurality of objects in a workspace and then, for each detected object of the plurality of objects, determines a sanitation score for the detected object based on observations of the detected object over a time period. The sanitation system generates a cleaning schedule based on the sanitation score for each detected object, wherein the cleaning schedule comprises instructions for a sanitation robot to clean the plurality of objects.

Abstract: Disclosed herein are systems and methods for detecting and mitigating inappropriate behavior. The systems and methods may include receiving data. Using the data a harassment score and/or classification for a behavior may be determined. Using the harassment score and/or classification, a determination may be made as to when the harassment score and/or classification for the behavior exceeds a threshold. When the threshold is exceeded, a protection system and/or action engine may be activated to mitigate the inappropriate behavior.

Abstract: A vehicle recognition system includes a sound analysis circuit to analyze captured sounds using an audio machine learning technique to identify a sound event. The system includes an image analysis circuit to analyze captured images using an image machine learning technique to identify an image event, and a vehicle identification circuit to identify a type of vehicle based on the image event and the sound event. The vehicle identification circuit may further use V2V or V2I alerts to identify the type of vehicle and communicate a V2X or V2I alert message based on the vehicle type. In some aspects, the type of vehicle is further identified based on a light event associated with light signals detected by the vehicle recognition system.