Robot Dexterity

Robots ‘ability to cope with a variety of objects and actions challenges our understanding of how such skills should be represented and how representations in different ways can be coordinated.
Finally, the introduction of such robots so far that they interact with humans can help to combine the findings of neuroscience with robotic research into fast learning mechanisms and social science insights into human robot collaboration.

Computers and digital robots have further highlighted the multi – purpose robotic manipulators and the obvious challenge of using anthropomorphic robotic hands to bring robot dexterity.
A bimanual research system with a pair of anthropomorphic robotic hands ( Dextrous ‘Shadow Hand ) mounted on robotic arms ( PA – 10 ) for positioning.

Recent advances in machine learning, large Data and robotic perception have put us at the threshold of a quantum leap in robot ability to perform motor – related tasks and function in uncontrolled environments, “says plate.
As part of a NASA grant, the Platts laboratory recently built a robot with touch sensors and developed new algorithms for the interpretation of touch data.
Engineering professor Hanumant Singh, in collaboration with Platt, builds a mobile robot – sized golf cart with a robot arm that can travel independently on campus and perform simple manipulation tasks such as removing waste.

Therefore, the addition of neuroscientific experiments with computational modelling, computer simulations and experiments with real robotic hands can be an invaluable source of additional information for the analysis of various assumptions concerning the control of the capture action.
A guiding idea is to conceptualize the capture as essentially a multi – stage mapping problem : the visual system extracts an initial representation that is then transformed into separate mapping paths to the location of objects and the characteristics of the relevant objects, such as shape, size and orientation.

Instead of getting capture point candidates out of a mathematical optimization program, researchers use a training classifier to assign capture point locations based on visual inputs of objects.
At first glance, it seems to be a brute force method, but it does allow for various optimizations, such as the technique of decomposition of shapes, to improve the ability to generalize from stored to new forms of objects.

It is difficult to say how much skill you need for a particular robot application – or even if “how much” is the right question.
An isolated factor does not necessarily define the robot as “skillful” ( for example, a fast robot is not always capable of handling ), but together it provides an overview of the ability to perform the task.
The different characteristics of the robot and its gripper combine to determine the ability of the system.

Dactyl learns to solve the task of reorienting objects completely in simplification without human input.
Simulated robots can easily provide enough data to train complex policies, but most manipulation problems cannot be accurately modeled so that such policies can be transferred to real robots.

Training directly on physical robots allows politics to learn from physics in the real world, but today’s algorithms would require years of experience to solve a problem such as reorienting objects.
By building simulations to carry out support, we have reduced the problem of controlling a robot in the real world to perform a simulation task, which is a good solution for strengthening training.
Dactyl lab set up with Shadow hands, motion tracking cameras and Basler RGB cameras.

Nothing in the world – animals or robots – is approaching the flexibility and agility of the human hand.
You can’t wait until the robotic arms get confused for years of practice, and it’s hard to get a world simulation, precise enough for training purposes.
For OpenAI, the task they had set was to teach a robot to manipulate a six – sided cube, moving it from one position to another, so that a certain side was facing upwards.

Although there are many algorithms of “smooth” planning and optimization of movements, the typical manipulation sequences are of a hybrid nature, combining fluid state changes in which the manual configuration changes continuously ( while maintaining its current contact pattern with the object ) with non – stop transitions that occur when the contact pattern changes.

The actual robotic implementations can therefore offer the possibility of a useful “intermediate” level of abstraction, which allows us to “sketch” the appropriate processing architecture for manual operation in such a way that it is sufficiently detailed that it can be validated in relation to its calculation capacity and thus help in mapping potential functional capacity.
The manipulation of such objects could also pave the way to combine observations about dextrous manipulation with a deeper understanding of higher cognition, without which there would be no manipulation of such objects.