Method and system for determining a joint in a virtual kinematic device

A method for determining, by a data processing system, a joint in a virtual kinematic device, the virtual kinematic device being a virtual device having at least one kinematic capability that is defined by at least two links of the virtual device and a joint connecting the two links, and the joint being defined by a joint type and by a joint descriptor for defining motion capabilities of a specific joint type;

• the method comprising:
• receiving input data containing data on two point cloud representations of two given links of a given virtual kinematic device;
• applying a joint type analyzer to the input data, the joint type analyzer being modeled with a function trained by a machine learning (ML) algorithm and the joint type analyzer generating intermediate data;
• providing the intermediate data containing data for selecting a specific joint type associated with the two given links;
• applying the selected specific joint descriptor analyzer to the input data, the specific joint descriptor analyzer being modeled with a function trained by a ML algorithm and the specific joint descriptor analyzer generating output data;
• providing the output data containing specific joint descriptor data for determining the mutual motion capabilities of the specific joint type associated with the two given links;
• and
• determining from the output data at least one joint in the virtual kinematic device.

A method for determining, by a data processing system, a joint in a virtual kinematic device, the virtual kinematic device being a virtual device having at least one kinematic capability that is defined by at least two links of the virtual device and a joint connecting the two links, and the joint being defined by a joint type and by a joint descriptor for defining motion capabilities of a specific joint type;

• the method comprising:
• receiving input data containing data on two point cloud representations of two given links of a given virtual kinematic device and data on the specific joint type associated with the two links;
• applying a specific joint descriptor analyzer to the input data, the specific joint descriptor analyzer being modeled with a function trained by a machine learning (ML) algorithm, and the specific joint descriptor analyzer generating output data;
• providing the output data containing specific joint descriptor data for determining the mutual motion capabilities of the specific joint type associated with the two given links;
• and
• determining from the output data at least one joint in the virtual kinematic device.

A method for providing, by a data processing system, a trained function for identifying a joint type in a virtual kinematic device, the virtual kinematic device being a virtual device having at least one kinematic capability and the kinematic capability being defined by at least two links of the virtual device and a joint connecting the two links, and the joint being defined by a joint type and by a joint descriptor for defining motion capabilities of a specific joint type;

• the method comprising:
• receiving input training data containing data on a plurality of two point cloud representations of two given links of a plurality of virtual kinematic devices;
• receiving output training data containing, for each of the plurality of two point cloud link representations, data for determining a specific joint type associated with the two given links, the output training data being related to the input training data;
• training a function based on the input training data and the output training data via a machine learning (ML) algorithm;
• and
• providing the training function for modeling a joint type analyzer.

A method for providing, by a data processing system, a trained function for identifying a joint descriptor in a virtual kinematic device, the virtual kinematic device being a virtual device having at least one kinematic capability and the kinematic capability being defined by at least two links of the virtual device and a joint connecting the two links, and the joint being defined by a joint type and by a joint descriptor for defining motion capabilities of a specific joint type;

• the method comprising:
• receiving input training data containing data on a plurality of two point cloud representations of two given links of a plurality of virtual kinematic devices;
• receiving output training data containing, for each of the plurality of two point cloud link representations, specific joint descriptor data for determining mutual motion capabilities of the specific joint type associated with the two given links;
• training a function based on the input training data and the output training data via a machine learning (ML) algorithm;
• and
• providing the trained function for identifying a joint descriptor being a joint descriptor analyzer.

A data processing system, comprising:

• a processor;
• and
• an accessible memory, the data processing system being configured to:
• receive input data containing data on two point cloud representations of two given links of a given virtual kinematic device and data on a specific joint type associated with the two given links;
• apply a specific joint descriptor analyzer to the input data, the specific joint descriptor analyzer being modeled with a function trained by a machine learning (ML) algorithm and the specific joint descriptor analyzer generating output data;
• provide the output data containing specific joint descriptor data for determining mutual motion capabilities of the specific joint type associated with the two given links;
• and
• determine from the output data at least one joint in the virtual kinematic device.

A data processing system, comprising:

• a processor;
• and
• an accessible memory, the data processing system being configured to:
• receive input training data, the input data containing data on a plurality of two point cloud representations of two given links of a plurality of virtual kinematic devices;
• receive output training data related to the input training data, the output training data containing, for each of the plurality of two point cloud link representations, data for determining a specific joint type associated with the two given links;
• train a function based on the input training data and the output training data via a machine learning (ML) algorithm to form a training function;
• and
• provide the training function for modeling a joint type analyzer.

A data processing system, comprising:

• a processor;
• and
• an accessible memory, the data processing system being configured to:
• receive input training data, the input training data containing data on a plurality of two point cloud representations of two given links of a plurality of virtual kinematic devices;
• receive output training data, the output training data containing, for each of the plurality of two point cloud link representations, specific joint descriptor data for determining the mutual motion capabilities of a specific joint type associated with the two given links;
• train a function based on the input training data and the output training data via a machine learning (ML) algorithm to form a trained function;
• and
• provide the trained function for modeling a joint descriptor analyzer.