JinZone

Hybrid artificial intelligence framework for robotic platforms

Smarter and safer robots

JinZone is a hybrid artificial intelligence framework with Elif Lab’s proprietary semantic layer for controlling, moving and orchestrating the activities of robotic platforms.

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Readable and transparent activities

JinZone makes robot activities readable and transparent, expressed in real time through natural language. An additional element of safety and controllability to integrate robots within dynamic and complex environments.

Complex tasks management

JinZone makes the robot capable of handling and executing articulated tasks and achieving complex goals even when they are formulated in an abstract manner by the user.

Awareness inside

JinZone uses all on-board sensors (camera, GPS, IMU, Lidar...) to monitor its surroundings. It reconciles stimuli relating to the same object with the corresponding semantic concept. The object therefore incorporates characteristics that are taken into account by the robot in the management of its activities.

Use of natural language

Communication between humans and machines is made easier and more meaningful through the two-way use of natural language. The robot accepts instructions in natural language and transparently states its 'reasoning'.

Hybrid training

JinZone harmoniously manages learning both through training and through the introduction of abstract rules by the user. This makes it possible to introduce robots into new environments, where not enough data may have already been collected for training.

Fleet management

The abstract semantic layer makes it possible to co-ordinate different robots and fleets composed of different models, in order to use multiple resources to perform and manage complex tasks.