Amazon Technologies, Inc.

Amin Hani Atrash

Saurabh Gupta

Raumi Sidki

Sandeep Samdaria

Roger Robert Webster

Karim Helwani

Yelin Kim

Vikas Deshpande

Morelle Sheer Arian

Shi Bai

Aarthi Raveendran

Sven Cremer

Alexander Caughron

Kevin V Macwan

Xiaowen Mao

A robot that is able to move about an environment determines a wait location in the environment to wait at when not otherwise in use. The wait location may be selected based on various factors including position of objects, next scheduled use, previous usage of the robot, availability of wireless connectivity, user traffic patterns, user presence, visibility of the surrounding environment, and so forth. The robot moves to that location and maintains a pose at that location, such as orienting itself to allow onboard sensors a greatest possible view of the environment. If a wait location is occupied, the robot may move to another wait location.

Dynamic wait location for an autonomous mobile device

A robot moves about an environment and may interact with a user. A waypoint specifies where the robot is to move to with respect to the user while a proxemic cost map is used to plan the path to the waypoint. User input or preferences may be used to modify the waypoint or the proxemic cost map. The waypoint may specify a particular distance and bearing with respect to the user. The proxemic cost map may be oriented with respect to the user and specifies costs for particular areas. For example, an area immediately behind the user may have a very high cost while an area in front of the user may have a low cost. Based on the waypoint and the proxemic cost map, a path is selected and the robot moves along that path, avoiding the high cost areas in favor of the low cost areas if possible.

System for dynamic positioning of an autonomous mobile device with respect to a user

A system may perform adaptive target presence probability to predict a location of a user (e.g., target) at a given time based on accumulated observations. For example, the system may track a location of the user over time and generate observation data associated with a user profile. The observation data may include a plurality of observations, with a single observation corresponding to a location and time at which the user was detected. The system may apply a clustering algorithm to the observation data to generate probability distributions (e.g., clusters) associated with discrete locations. For example, the system may group observations that are in proximity to each other and separate the groups based on location. Using the probability distribution for an individual cluster, the system may determine a likelihood that the user is present at a location corresponding to the cluster.

Adaptive target presence probability estimation

A robot uses types of behavior such as approach, follow, avoid, and so forth to move about an environment and interact with a user. An occupancy map provides information about obstacles in the environment. A predicted trajectory of the user is determined that is indicative of expected locations and confidence of those expected locations. The predicted trajectory may be based on the user's movement and the occupancy map. Based on the predicted trajectory and the occupancy map, a target point and a path to the target point is determined. The path may also be based on a proxemic cost map that specifies how regions with respect to the user may be traversed.

Dynamic positioning of an autonomous mobile device with respect to a user trajectory

A robot has a component that is oriented relative to a chassis of the robot. For example, a display screen is mounted to the robot using a pan and tilt arrangement. During operation, trajectory data indicative of a trajectory is determined that specifies where the robot is intended to travel as it moves through an environment. This trajectory data is used to dynamically orient the component. For example, as the robot moves through the environment, the display screen would be oriented such that it appears to be 'facing' a point in space that is associated with a point on the trajectory.

Dynamic trajectory-based orientation of autonomous mobile device component

A simulated emotional state of a device is generated and maintained based on previous emotional state and contextual information. A trigger is associated with an effect value. For example, successful completion of a task may have an effect value of +5 while task failure may have an effect value of &#x2212;6. Upon occurrence of a trigger, an associated effect value of the trigger may be combined with a previously determined effect value to determine a function input value (FIV). The FIV is used as input to a function that provides an emotion value as output. The emotion value may then be used as input to determine a particular action. For example, if the emotion value corresponds to 'happy', the resulting action may be presenting an animation of a smile on a display device. As triggers occur, the function input value is updated, resulting in updates to the emotion value.

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Los Altos, CA, United States of America

Amin Hani Atrash