Quantum Computer and Humanoid Robotics & AI Tech Make Incredible Breakthrough

Breakthrough Humanoid Robotics Technology: The Development of Biomimetic Hands

Clone Robotics, a leader in the field of robotics, has recently developed breakthrough humanoid robotics technology with the creation of their biomimetic hand. Model number v15 is the first of its kind, designed to mimic the appearance and movement of a human hand with hydrostatic muscles that move under transparent skin. These muscles have been created based on the concept of a McKibben muscle, which is essentially a mesh of tubes with balloons inside.

The team at Clone Robotics has created a muscle design that functions electronically, forgoing the use of large pumps externally. With the goal of creating a controllable muscle, balloons have been filled with acetaldehyde and stimulated with electric currents to induce contraction. This design has resulted in a robotic arm with approximately 27 degrees of freedom, allowing for a wide range of movement and control.

The Prototype in development is currently using a hydraulic setup to activate the muscles, with pressure being distributed via a 500-watt pump running at 145 PSI. The built-in magnetic sensors send information to the onboard artificial intelligence, which then adjusts the joint’s angles and velocity. The company plans to start selling their robotic hands by the end of the year, but the exact cost has not been revealed.

However, the breakthroughs of Clone Robotics do not stop here. Their next project is the development of a full robotic torso with a spine, including 124 muscles located in the hands, neck, shoulders, chest, and upper back. The aim is to integrate this into Clone Robotics’ Locomotion platform to carry the unit’s battery pack.

New General Robotic Arm Manipulation Prompt-Based Learning

Natural language processing in robotics has been recognized as an efficient method for executing tasks. However, task specification traditionally takes various forms, such as one-shot demonstrations, imitation, and following instructions in a language to achieve visual goals. These are typically viewed as different jobs that require specialized machine learning models.

To address this issue, AI researchers from Nvidia, Stanford, Caltech, Tsinghua, and UT Austin have developed a model of language that can be instructed to execute any task specified through input prompts. They designed an AI agent based on transformer neural networks, a generalist robot, and VIMA, which processes prompt signals and then outputs motor-related actions in an auto-regressive manner to train and evaluate the AI model.

The researchers developed a simulation benchmark with thousands of procedurally generated tabletop tasks with multimodal prompts over 600,000 expert trajectories for imitation learning and four levels of evaluation protocol for systematic generalization. VIMA is superior to previous methods, scaling up in terms of AI model capacity, size of data, and being 2.7 times more efficient than the next best alternative with ten times less training data.

Quantum Computer Breakthrough: The Development of Programmable Solid-State Superconducting Processor

Scientists at Arizona State University and XI Jiang University in China, together with two researchers in the UK, have managed to show that large amounts of quantum bits or qubits can be tuned to interact with one another and maintain coherence for an unimaginably long period of time in a programmable solid-state superconducting processor. This breakthrough was achieved by the development of quanta mini-body-scarring states (QMBS), which is a powerful method of maintaining coherence.

QMBSs provide the option of creating a vast multipartite entanglement for use in a range of quantum computing tasks. The study focuses on understanding how to delay thermalization to preserve coherence, regarded as a crucial research goal for quantum computing. This breakthrough could bring about a paradigm shift of silico-biological computational platforms that exceed the performance of classical Silicon Hardware. Theoretically, synthetic biological intelligence could emerge prior to artificial general intelligence (AGI).

In Vitro Neurons and the Development of Artificial Biological Intelligence

Researchers have been investigating the integration of digital systems with neurons for some time to create performances that are not possible using silicon. With the recent development of dish-brain technology, researchers have integrated in vitro neural networks from rodent or human origins into in silico computing using an array of high-density multiple electrodes. These cultures are then embedded into a simulated game-world that mimics an arcade version of POM. Using implications from the theory of active inference, the researchers observed apparent learning within five minutes of real-time gameplay that wasn’t evident under control conditions.

The ability of cultures to self-organize their activities in a purpose-driven manner when confronted with a lack of data about the consequences of their actions is known as artificial biological intelligence. Future applications could offer further insight into the cell-based correlation of intelligence, utilizing the computing power of living neurons to produce synthetic biological intelligence, which was once confined to the world of science fiction.


The breakthrough developments of Clone Robotics, researchers at Nvidia, scientists at Arizona State University and XI Jiang University in China, and the dish-brain technology team have all contributed to the continued growth in the field of robotics and artificial intelligence. The development of biomimetic hands, the creation of QMBSs, and the integration of in vitro neurons bring us one step closer to creating synthetic biological intelligence, revolutionizing the way we approach computation and intelligence.

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