Honda Motor Co., Ltd.

Nawid Jamali

Karankumar Patel

Soshi Iba

Huckleberry Febbo

Itoshi Naramura

Akinobu Hayashi

Snehal Dikhale

Hifza Javed

Daksh Dhingra

According to one aspect, behavior generation for situationally-aware social robots may include generating a synthesized behavior based on a latent space representation indicative of a situational context including a situationally-aware social robot and one or more individuals, performing behavior retargeting on the synthesized behavior based on a real behavior from an initial latent space input to generate a retargeted behavior, and performing the retargeted behavior. The generating the synthesized behavior and the performing behavior retargeting may be implemented based on a generative adversarial network (GAN) based network. The GAN based network may be trained using a multimodal human behavioral dataset.

Behavior generation for situationally-aware social robots

Systems and methods for visuo-tactile object pose estimation are provided. In one embodiment, a computer implemented method includes receiving image data, depth data, and tactile data about the object in the environment. The computer implemented method also includes generating a visual estimate of the object that includes an object point cloud. The computer implemented method further includes generating a tactile estimate of the object that includes a surface point cloud based on the tactile data. The computer implemented method yet further includes estimating a pose of the object based on the visual estimate and the tactile estimate by fusing the object point cloud and the surface point cloud in a 3D space. The pose is a six-dimensional pose.

Systems and methods for visuo-tactile object pose estimation

Systems and methods for tactile output estimation are provided. In one embodiment, the system includes a depth map module, an estimation module, and a surface module. The depth map module is configured to identify a region of interest (RoI) of an object. The area of the RoI corresponds to a tactile sensor size of a tactile sensor. The depth module is further configured to receive depth data for the RoI from a depth sensor and generate a depth map for the RoI based on a volume of the depth data relative to a frame of reference of the RoI. The estimation module is configured to estimate a tactile sensor output based on the depth map. The surface module configured to determine surface properties based on the estimated tactile sensor output.

Systems and methods for estimating tactile output based on depth data

Systems and methods for visuo-tactile object pose estimation are provided. In one embodiment, a method includes receiving image data about an object and receiving depth data about the object. The method also includes generating a visual estimate of the object based on the image data and the depth data. The method further includes receiving tactile data about the object. The method yet further includes generating a tactile estimate of the object based on the tactile data. The method includes estimating a pose of the object based on the visual estimate and the tactile estimate.

Methods, grasping systems, and computer-readable mediums storing computer executable code for grasping an object are provided. In an example, a depth image of the object may be obtained by a grasping system. A potential grasp point of the object may be determined by the grasping system based on the depth image. A tactile output corresponding to the potential grasp point may be estimated by the grasping system based on data from the depth image. The grasping system may be controlled to grasp the object at the potential grasp point based on the estimated tactile output.

Growing community of inventors

Dublin, CA, United States of America

Nawid Jamali