The article introduces Jev, a fast, cheap AI model from TypeSafe that handles reflexive decisions while LLMs handle reasoning, showcasing five demos: context compression, ad analysis, real-time Mario gameplay, probability-based animations, and autonomous rocket landing — all at fractions of LLM cost and latency.
Tokyo-based Enactic_ai fully open-sources OpenArm, a 7-DOF humanoid robotic arm including 3D-printable CAD files, BOM, motor firmware, ROS2 drivers, web-based MuJoCo simulation, Isaac Sim integration, and a leader arm for zero-latency force-feedback teleoperation, dramatically lowering the cost barrier for embodied AI research.
This guide walks through training custom locomotion policies for the $399 Microduck bipedal robot using reinforcement learning in MuJoCo simulation on UCloud GPU instances, covering environment setup, walking and running policy training, ONNX export, simulation validation, and Hugging Face deployment.
Domain-Adaptive Diffusion Policy (DADP) decouples representation learning and injects domain information into the diffusion process, enabling robots to adapt across varying friction, mass, and dynamics, achieving strong zero-shot performance on MuJoCo and Adroit benchmarks, especially in out-of-distribution scenarios.
DexJoCo, a new MuJoCo‑based benchmark from the Chinese Academy of Sciences, introduces 11 complex dexterous‑hand tasks spanning tool use, bimanual collaboration, long‑horizon execution, and reasoning, and reveals that even state‑of‑the‑art robot learning models still struggle with reliable fine‑grained manipulation.
The article details how Eon Systems reconstructed a complete fruit‑fly brain with 125,000 neurons and 50 million synapses, integrated it into a MuJoCo‑simulated body, and demonstrated natural behaviors, while outlining the roadmap toward mouse and human brain uploads and the broader implications for AGI and digital immortality.
In March 2026, Eon Systems announced the first ever multi‑behavior whole‑brain simulation of a fruit fly, recreating its 125,000 neurons and 50 million synapses in a 1:1 digital model that drives a physical body via a closed‑loop perception‑neural‑action system, outperforming random‑graph controls and sparking debate over consciousness.
ENOTO introduces ensemble Q‑networks into the offline‑to‑online reinforcement‑learning pipeline, using minimum‑Q and uncertainty‑driven exploration to stabilize fine‑tuning, boost learning efficiency, and achieve 10‑25 % higher cumulative returns with minimal online interaction across MuJoCo and AntMaze benchmarks.
