Genesis AI unveils GENE-26.5, a robotic brain designed to help general-purpose robots perform complex physical tasks with human-level dexterity and coordination.

Developing a general robot manipulation system capable of performing a wide range of tasks in complex, dynamic, and unstructured real-world environments has long been a challenging task. It is widely recognized that achieving human-like efficiency and robustness manipulation requires the robotic brain to integrate a comprehensive set of functions, such as task planning, policy generation, anomaly monitoring and handling, and long-term memory, achieving high-efficiency operation across all functions. Vision-Language Models (VLMs), pretrained on massive multimodal data, have acquired rich world knowledge, exhibiting exceptional scene understanding and multimodal reasoning capabilities. However, existing methods typically focus on realizing only a single function or a subset of functions within the robotic brain, without integrating them into a unified cognitive architecture. Inspired by a divide-and-conquer strategy and the architecture of the human brain, we propose FrankenBot, a VLM-driven, brain-morphic robotic manipulation framework that achieves both comprehensive functionality and high operational efficiency. Our framework includes a suite of components, decoupling a part of key functions from frequent VLM calls, striking an optimal balance between functional completeness and system efficiency. Specifically, we map task planning, policy generation, memory management, and low-level interfacing to the cortex, cerebellum, temporal lobe-hippocampus complex, and brainstem, respectively, and design efficient coordination mechanisms for the modules. We conducted comprehensive experiments in both simulation and real-world robotic environments, demonstrating that our method offers significant advantages in anomaly detection and handling, long-term memory, operational efficiency, and stability -- all without requiring any fine-tuning or retraining.

Embodied AI focuses on the study and development of intelligent systems that possess a physical or virtual embodiment (i.e. robots) and are able to dynamically interact with their environment. Memory and control are the two essential parts of an embodied system and usually require separate frameworks to model each of them. In this paper, we propose a novel and generalizable framework called LLM-Brain: using Large-scale Language Model as a robotic brain to unify egocentric memory and control. The LLM-Brain framework integrates multiple multimodal language models for robotic tasks, utilizing a zero-shot learning approach. All components within LLM-Brain communicate using natural language in closed-loop multi-round dialogues that encompass perception, planning, control, and memory. The core of the system is an embodied LLM to maintain egocentric memory and control the robot. We demonstrate LLM-Brain by examining two downstream tasks: active exploration and embodied question answering. The active exploration tasks require the robot to extensively explore an unknown environment within a limited number of actions. Meanwhile, the embodied question answering tasks necessitate that the robot answers questions based on observations acquired during prior explorations.

An Oxford University spin-off has developed the technology to control automated cars, but it doesn't make hardware, rather providing the "brains" of the vehicle. Oxbotica's "brain" – called Selenium – can recognise its location and surroundings and, crucially, it can be applied to anything that moves – not just cars. The company's co-founder, Paul Newman, explained the technology could be used in anything from space ships to fork lift trucks in warehouses. It's because the company has invested so much into the research behind the technology that it's become such an innovative solution. "We never called ourselves a driverless car group," Posner told the FT. That has applications in all places."

A super-intelligent robotic "brain" that can learn new skills by browsing millions of web pages has been developed by US researchers. Robo Brain is designed to acquire a vast range of skills and knowledge from publicly available information sources such as YouTube. The information it learns can then be accessed by robots around the world, helping them to perform everyday tasks. A similar project is already being developed in Europe. RoboEarth, described as a world wide web for robots, was demonstrated by researchers at Eindhoven University in the Netherlands in January.