By HoloWorld
We say there is a healthy mind in a healthy body. Till date the focus was on seeing both as an individual entity. Let’s consider a case of humanoid. On correlation the mind can be defined as an AI and the body would be the robot. We have designed them separate till this point. Let’s look at a point of integrating the same. Embodied AI has been described as the idea that the mind is not only connected to the body but that the body influences the mind. If we consider an advanced humanoid, the degree of kinesthetics have even reached complex movements such as jump, back flip etc. but in comparison with the intelligence of a simple animal like a dog, the humanoids lack this ability to learn new skills, adapt to new environment, emotional intelligence etc. which they can do it better.
Dog’s emotional intelligence
The last decade has seen a revolution in the Trinity of AI where the main components have come together to create the deep learning revolution. They are:
- Algorithms
- Data
- Infrastructure
Using a neural network framework, humans could use the data for optimizing a solution in a collective approach rather than individual approach. GPUs form the infrastructure where complex data could be handled parallel. This parallelism enabled autonomous machines to run up to a billion operations per sec.
Trinity of AI
Going forward the application of Embodied AI is used for AI problems for virtual robots that can manoeuvre, sense the visual and acoustic information, and interact in the virtual world and with other virtual robots — these simulated robot solutions are then transferred to real world robots.
Embodied AI can be applied for personal robotics, autonomous vehicles, and virtual assistants. Let’s look at some applications of this domain.
Avatar/Agents:
It’s mainly a character created for abstraction to respond or to take actions in the virtual environment. It can primarily be an egocentric character and the 3D environment created for the avatar to navigate. It can be simulated to represent the real world. The concept of reinforcement learning can be implemented here.
Avatar
Egocentric Perception:
The perspective to interact or interface with the 3D space with reference to the avatar either by using a hallucinated data or the first person’s view can be defined as Egocentric perception. Allocentric perception encodes the objects with corelated to another object. Avatars or embodied agent won’t have access to a map, and they need to navigate the different rooms and locations in the environment to understand and create its address points.
Allocentric vs Egocentric perception
Active Perception:
An agent must move to succeed — controlling the pixels that it perceives as observations that the agent collects are a consequence of the actions that the agent takes in the environment. It should be spontaneous to visual variation.
Active perception
Sparse rewards:
It’s an expansion of reinforcement learning in which the avatar can get rewards for tasks succeeded in the 3-D environment which makes the avatar more responsive and gain intelligence like picking the right object, latching the gate, etc.
Sparse Rewards
Augmenting avatar to robots:
Training robots in the real world is time consuming, labour intensive and expensive process which can also fail abruptly. As an alternative, we can create embodied agent simulating a realistic environment and transfer its actions into the real robots and making it more autonomous. Once the approach is approved the embodied intelligence can help speed up for the actual robot learning.
Augmenting avatar to robots
On the application level, Roomba robots have gained better functionality by detecting the map of the 3-D space, identification of planted and scattered objects and realigning its path of movement. This gets optimised into a critical path for effective cleaning.
A classical case of weed management was implemented by John Deere in which the autonomous sprayer can identify the weeds and the leaves which can optimise the spray quantity and path of travel.
Embodied AI is found to have increasing clinical relevance for therapeutic applications in mental health services. With innovations ranging from ‘virtual psychotherapists’ to social robots in dementia care and autism disorder, artificially intelligent virtual and robotic agents are increasingly taking on high-level therapeutic interventions that used to be offered exclusively by qualified and trained professionals.
The future ahead for Embodied AI is looking very promising and the outcome of several research studies and experimentation has given breakthrough in embodiment at various complexities. While the classical approach has been found to resolve problems for applications in terms of intelligent algorithms, the embodied approach is being adopted which can make the problem solving more autonomous.
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