Amazon Technologies, Inc.

Massachusetts Institute of Technology

Toyota Research Institute, Inc.

Moses Theodore Ort

Andrew Stubbs

Andrew Dominic Marchese

John Gregory Longtine

Russell Toris

Sarath Krishnaswamy

Jacob Taylor Wachlin

Guy Rosman

Daniela Rus

Stephen G McGill

Luke S Fletcher

John J Leonard

Sertac Karaman

Igor Gilitschenski

Alyssa Pierson

Minoru Brandon Araki

Systems and methods described herein relate to estimating risk associated with a vehicular maneuver. One embodiment acquires a geometric representation of an intersection including a lane in which a vehicle is traveling and at least one other lane; discretizes the at least one other lane into a plurality of segments; determines a trajectory along which the vehicle will travel; estimates a probability density function for whether a road agent external to the vehicle is present in the respective segments; estimates a traffic-conflict probability of a traffic conflict in the respective segments conditioned on whether an external road agent is present; estimates a risk associated with the vehicle following the trajectory by integrating a product of the probability density function and the traffic-conflict probability over the at least one other lane and the plurality of segments; and controls operation of the vehicle based, at least in part, on the estimated risk.

Systems and methods for estimating the risk associated with a vehicular maneuver

A grasp management system and corresponding methods are described. In some examples, information about a set of grasps of a robotic manipulator is accessed. The information is used by the robotic manipulator to attempt to grasp an item using the set of grasps associated with a first grasping orientation. An orientation of the robotic manipulator can be adjusted into a second grasping orientation and the robotic manipulator can attempt to grasp the item using the set of grasps associated with the second grasping orientation. Information about the attempts can be recorded and used to determine a richness measure that may represent a richness of the set of grasps for the item.

Feature identification and extrapolation for robotic item grasping

A grasp management system and corresponding methods are described. In some examples, information about a grasp by which an end of arm tool of a robotic manipulator successfully grasped an item is accessed. The grasp may be associated with contact points on the item. Other contact points may be simulated based on the contact points in order to define a grasping surface that includes at least a portion of the other contact points. Information about the grasping surface may be accessed to determine a primitive shape that represents a feature of the item. The primitive shape may be used determine other features on the item or other features on other items that may also be graspable by the end of arm tool or other end of arm tools.

A grasp management system and corresponding methods are described. In some examples, information about a grasp by which an end of arm tool of a robotic manipulator successfully grasped an item is accessed. The grasp may be associated with contact points on the item. Other contact points may be simulated based on the contact points in order to define a set of grasping surfaces that include at least a portion of the other contact points. Information about the grasping surfaces may be accessed to determine a primitive shape that represents a feature of the item. The primitive shape may be used determine other features on the item or other features on other items that may also be graspable by the end of arm tool or other end of arm tools.

A grasp management system and corresponding method are described. In some examples, it is determined whether an end of arm tool of a robotic manipulator is capable of grasping an item in a first orientation and a second orientation. Information regarding success of the attempts may be retained in a database. A richness measure for each of the first orientation and the second orientation may be determined based upon a spatial variation of successful attempted grasps in the respective orientation.

Validation of robotic item grasping

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Lakewood, NJ, United States of America

Moses Theodore Ort