Soft
Unless very carefully controlled, making contact between a hard gripper and an object leads to shocks that could damage the object. To overcome the challenges for delicate grasp actuation and high-resolution sensing, SoftGrip opts for solutions based on soft robotic structures comprised of materials that deform in a distributed and continuous manner.
Food-safe
Materials that come in contact with food, including “active” and “intelligent” materials must be approved for use by the European Food Safety Authority. All materials used for the soft gripper will be selected and synthesized according to the rules set by EU food-safe directives.
Intrinsic Sensing
The gripper configuration can be useful for determining whether a grasp is successful, whether a grasp is robust, and whether the object was grasped in the intended pose. In addition, the shape control of continuum robots requires means of sensing the curved shape of the robot and the internal.
Imitation Learning-based
A set of methodologies and machine learning algorithms will be implemented for transferring human skill to robots based on task observations. The SoftGrip learning-by-demonstration approach will build upon the use of multi-task and meta-learning techniques to achieve rapid skill transfer.
Self-repair
In industrial and production applications, the gripper must undergo thousands of cycles of operation without exhibiting any mechanical or functional damage. SoftGrip proposes to solve this weakness of soft material by constructing soft robotics from self-healing materials that permit healing microscopic and macroscopic damage.
Recyclable
Recyclability of the materials is important, since SoftGrip envisions that thousands of low cost SoftGrip grippers will be employed by the agri-food industry and therefore all materials used for the soft structure and the embedded sensors will be recyclable based on chemical recycling processes.
Embodied Intelligence
The functionalities of the soft gripper will be translated into physical intelligence, through the exploitation of intrinsic properties of the materials, the arrangement, and the geometrical features of the components of the gripper. This represents the framework of “embodied intelligence”, that allows “programming” functionalities in the body itself, limiting the processing burden required from control.
Low Cost
Cost is always a significant element when it comes to moving from manual labor to automation. This is particularly true in agriculture where profit margins are generally low. Soft gripper mechanism will be a practical solution only if it is low-cost and thus scalable. Hence, the materials selected should be low cost and the fabrication technologies should allow for economies of scale.
Soft
Unless very carefully controlled, making contact between a hard gripper and an object leads to shocks that could damage the object. To overcome the challenges for delicate grasp actuation and high-resolution sensing, SoftGrip opts for solutions based on soft robotic structures comprised of materials that deform in a distributed and continuous manner.
Food-safe
Materials that come in contact with food, including “active” and “intelligent” materials must be approved for use by the European Food Safety Authority. All materials used for the soft gripper will be selected and synthesized according to the rules set by EU food-safe directives.
Intrinsic Sensing
The gripper configuration can be useful for determining whether a grasp is successful, whether a grasp is robust, and whether the object was grasped in the intended pose. In addition, the shape control of continuum robots requires means of sensing the curved shape of the robot and the internal.
Imitation Learning-based
A set of methodologies and machine learning algorithms will be implemented for transferring human skill to robots based on task observations. The SoftGrip learning-by-demonstration approach will build upon the use of multi-task and meta-learning techniques to achieve rapid skill transfer.
Self-repair
In industrial and production applications, the gripper must undergo thousands of cycles of operation without exhibiting any mechanical or functional damage. SoftGrip proposes to solve this weakness of soft material by constructing soft robotics from self-healing materials that permit healing microscopic and macroscopic damage.
Recyclable
Recyclability of the materials is important, since SoftGrip envisions that thousands of low cost SoftGrip grippers will be employed by the agri-food industry and therefore all materials used for the soft structure and the embedded sensors will be recyclable based on chemical recycling processes.
Embodied Intelligence
The functionalities of the soft gripper will be translated into physical intelligence, through the exploitation of intrinsic properties of the materials, the arrangement, and the geometrical features of the components of the gripper. This represents the framework of “embodied intelligence”, that allows “programming” functionalities in the body itself, limiting the processing burden required from control.
Low Cost
Cost is always a significant element when it comes to moving from manual labor to automation. This is particularly true in agriculture where profit margins are generally low. Soft gripper mechanism will be a practical solution only if it is low-cost and thus scalable. Hence, the materials selected should be low cost and the fabrication technologies should allow for economies of scale.
