Robopsychologie

If you pick up a glass = your brain sends signals down your arm to move towards the glass and pick it up

If your arm is not there = no movement happens

Thus your brain keeps sending "move" signals

Mirror Therapy is one way to help with phantom limb pain

The rubber hand illusion can help to deal with this pain.

• Using VR Games that require the patients to move their limbs
  the amputated limb‘s image is filled in, in the VR Game = so they receive visual feedback

• Example: https://www.youtube.com/watch?v=VI621YPv9C0

Proprioception = bodily awareness

Robot proprioception is important for a variety of tasks:

• To avoid interference with objects / humans
• Avoid harming itself, others
• Bheave autonomously

Soter et al., 2018:

• Bodily aware soft robots: Integration of proprioceptive and exteroceptive sensors
• stimulated soft robot can learn to imagine its motion even when its visual sensor is not available

Kwiatkowski & Lipson, 2019:

• A robot modeled itself autonomously
• The robot had no prior knowledge about its shape
• The self-model was then used to perform tasks & detect self-damage

• measures values internally to the system (robot / autonomous car)
• e.g. motor speed, wheel load, direction, battery status

Inertial Measurement Units

• IMU are employed for monitoring the velocity and position changes
• tachometers are utlized for measuring speed and altimeters for altitude (Höhenlage)

1. hot
2. cold
3. pain
4. pressure
5. tickle 
6. itch
7. vibrations 
8. smooth
9. rough

Airskin - blue danube robotics: developed in Austria

• Industrial Robot with touch-sensitive skin
• Robot and gripper are fully covered with soft Airskin pads, including clamping (befestigen) shearing areas

• In the event of a collision between the robot and an employee or an object, the collision sensor responds and instantly triggers an emergency stop.

• Their finger can logalize touch with very high percision - less than 1mm - over a large, multicurved surface, much like its human counterpart
• Integrating the system onto a hand is easy: Thanks to this new technology the finger collects almost 1000 signals but only needs a 14 wire cable connecting it to the hand and it needs no complex off-board electronics.

• A capacitive pressure sensor system with 108 sensitive zones for the hands of the humanoid robot iCub was designed (Schmitz et al., 2010).

• The results show that the sensor can be used to determine where and (although to a lesser extent) how much pressure is applied to the sensor.

• the palm has 48 taxels
• each of five fingertips has 12 taxels
• fingertip has a shape similar to human fingertip
• the fingertip is small
• the fingertip provides 12 pressure measurements
• and is intrinsically compliant

• equipped with human-like fingernails and fingerprints
• able to use contact detection
• able to adjust force
• mimicking sensation of vibrations, textures and temperatures
• the hand and reflexes connect on their own

Users have an unprecedented ability to pick up objects without having to actively think about the amount of force they are applying - just like te reflexes of a real human hand

• It is a Tactile Telerobot that can fuse your own hand movements with the robot hands

• It can be used for a variety of tasks and gives realistic touch feedback

1. Communication between body and brain must be intact
2. Technical aspect has to be melted with the body so it becomes a body part
3. The additional body part has to improve the human's senses / capabilites

• part of the Cyborg community
• he has an antenna implanted which helps him to perceive vicible and invisible colours via audible vibrations in his skull
• these colours include infrareds and ultraviolets
• he can also receive colours from space, images, videos music or phone calls directly into his head via internet connection

• She developed the Seismic Sense: an online seismic sensor once implanted in her feet that allowed her to perceive earthquakes taking place anywhere in the planet through vibrations in real time.

• Ribas’ seismic sense also allowed her to feel moonquakes, the seismic activity on the Moon

• Ribas believes that by extending our senses to perceive outside the planet, we can all become senstronauts. Adding this new sense allowed her to be physically on Earth while her feet felt the Moon.

Pinna: Collects and amplifies the sound waves

Auditory Canal: The soundwaves from the pinna are deflected into the auditory canal

Eardrum: vibrates and sends the vibration onto the ossicles

Ossicles (Hammer, Anvil (Amboss), Stirrup (Steigbügel))

Cochlea: Snail shaped: Contains lympathic fluid within this fluid is the basilar membran which has hair cell attached to it

◦ The brain is able to focus the auditory attention on a particular stimulus

◦ Simultaneously, it is filtering our other auditory stimuli

◦ An example for this: hearing your own name in a noisy room

They trained "multi-stream convolutional neural network“

• When trained the system is now capable of focusing on a single voice and filtering out everything else. 

• This means that a trained system might be even better at filtering out noises compared to a human...

• Physiological variations in an emotionally aroused speaker can cause an increase of the subglottal pressure (the pressure generated by the lungs beneath the larynx), which can affect voice amplitude and frequency = expression of the speaker’s emotional state

• Expression of emotions through prosodic modulation of the voice, in combination with other communication channels, is crucial for affective and attentional regulation in social interactions in adults and infants (Sander et al., 2005; Schore and Schore, 2008).

Difficulties in understanding prosody, intentions and humour from spoken language.

• This can cause misinterpretations of spoken language Missy Cummings, an associate professor at MIT said:

“You could do all the machine learning in the world on the spoken word, but sarcasm is often in tone and not in word,” she added. “[Or] facial expressions. Sarcasm has a lot of nonverbal cues.”

A new AI assistant, OTO is specialising on this and can be used to assist in real-time conversational data:

“OTO is moving beyond speech-to-text, pioneering the first multi-dimension conversational system with the ability to merge both words + intonation (Acoustic Language Processing). This provides a much richer understanding of the context, sentiment and behaviors of a conversation.“

• AI algorithms can now more accurately detect depressed mood using the sound of a person‘s voice, according to new research at the University of Alberta

• An App could collect voice samples from people over longer time periods when they speak naturally

• Over time, the App would track indicators of mood

• Montreal-based AI startup Lyrebird provides an online platform that can mimic a person’s mimics speech when trained on 30 or more recordings

• Baidu introduced a new neural voice cloning system that synthesizes a person’s voice from only a few audio samples

• New Github project (Sept., 2019): Users enter a short voice sample and the model — trained only during playback time — can immediately deliver text-to-speech utterances in the style of the sampled voice

A managing director of a British energy company, believed that his boss was on the phone.

◦ He followed orders on a Friday afternoon in March 2019 to wire more than $240,000 to an account in Hungary.
◦ It was not his boss on the phone but someone using ultrareslistic voice cloning.

• A decoder transforms brain activity patterns produced during speech into movements of the virtual vocal tract.

• A synthesizer converts these vocal tract movements into a synthetic approximation of the participant’s voice.

• This new synthetic speech should be able to build a device that is clinically viable in patients with speech loss.

1. it is suppressed

2. Faces are not treated as a combination of different parts. Faces are processed as “wholes“
   A judgment about the upper half changes depending on the lower half of the face

• African American (AA) and European American (EA) participants saw images of the same ethnicity faces and other ethnicity faces

• Participants showed stronger FFA (fusiform face area) activation for the same ethnicity faces

• We can recognise familiar faces despite...

• Showing different emotional expressions
• Seeing them in different angles

• Large configural changes leave recognition unharmed

• Recognition of familiar faces is remarkably robust under a range of deformations:

• Individuals on the left can be recognized if they are familiar to us despite changes to metric distances between facial features

The blue/black dress problem can be explained with a phenomenon called colour constancy which is the way that our brains interpret colours.

What you see in the picture depends on your individual perception and where you see it:

• Shadows are interpreted differently by our visual system
• When the shadows are removed: the colour is perceived differently

• The brain has developed a "rulebook" of how objects should look from past experiences
• When interpreting new information, our brain relies on previous knowledge
• the brain takes a "shortcut" to focus on important aspects
• optical illusions fool our brains by taking advantage of these shortcuts

• 9-month-old infants were able to pass rapid categorization of human and ape faces (Peykarjou et al., 2017)

• Perceptual categorization of cat and dog silhouettes by 3- to 4-month-old infants (Quinn, Eimas & Tarr, 2001)

• Generative Adversarial Networks (GANs) revealed a new challenge for image classification: Adversarial Images
• Adversarial Images are images whose class category looks obvious to a human but causes massive failures in a deep network
• with only a minor disortion (seemingly) a deep network's classification of the image goes from a panda to a gibbon

• It is necessary to highlight the fundamentally flawed ways that ImageNet classifies people in “problematic” and “offensive” ways.

• It is crucial to assess the fallibility of AI systems and prevalence of machine learning bias

• Given a single facial image, a classifier could correctly distinguish between gay and heterosexual men in 81% of cases, and in 71% of cases for women

• Human judges achieved much lower accuracy: 61% for men and 54% for women

• The accuracy of the algorithm increased to 91% and 83%, respectively, given five facial images per person.

• Facial features employed by the classifier included both fixed (e.g., nose shape) and transient facial features (e.g., grooming style).