Genesis AI's open simulation platform for physical AI: one Pythonic API over a multi-physics engine (rigid, FEM, MPM, PBD, SPH, Stable Fluid, IPC), the Nyx path tracer, and Quadrants, a cross-platform Python-to-GPU compiler. It is built to be the evaluation engine for robot foundation models rather than a data factory: 30,000 parallel environments at 43M FPS, and under a zero-shot real-to-sim protocol, sim-vs-real evaluation agreement of Pearson 0.8996 with mean maximum rank violation 0.0166. Apache 2.0, 29.9k stars.
Bimo is a 45 cm, roughly 1.6 kg hip-head biped kit: eight STS-3215 bus servos, a BNO08x 9-DoF IMU, four VL53L0X rangefinders, two 180-degree cameras, and a custom RP2040 board closing a 20 Hz control loop. The repository publishes the Python control API (1023 lines), three MCU firmware builds (1349 lines), a ROS2 wrapper (1431 lines) and an Isaac Lab training environment (874 lines: six reward terms, dense domain randomization, and a system-identified STS3215Actuator with 3.113 Hz bandwidth, directional gear backlash and a 5 ms bus delay), plus a 26MB Bimo.usd model. The CPG gait's 104 Fourier coefficients and per-joint amplitude gains are hardcoded in both Python and C, and a [256,128,64] PPO teacher distills into a [64,32] student that goes through onnx2c into the firmware, so the robot walks untethered on the MCU. All code is Apache-2.0, but the CAD and electronics the README promises are still marked coming soon: no STL, STEP, schematic, gerbers or BOM exist in the working tree or in git history, and neither release ships pretrained weights (zero assets) or a DIY assembly manual. Currently v1.1.0 with 198 stars, in pre-order status.
Open-source causal world model from Seedleap.ai: text prompts and keyboard actions alter world evolution in real time. Causal KV cache + four-step DMD sampling enable long-horizon real-time rollouts on a single GPU; No.1 real-time world model on WBench.
starVLA's representation-centric continued pre-training framework: shallow-layer protection, caption co-training, and OFT+PI+GR00T multi-head co-supervision build a Qwen3-VL-4B action backbone reaching 82.6% on LIBERO-Plus and 92.5% on RoboTwin 2.0 with cross-embodiment transfer.
MIT's VISTA gives a general-purpose multimodal model raw PNG observations, free-form language reasoning and a lossless archive of every environment frame, then lets it play ARC-AGI-3. With Claude Opus 5.0 it completes all 25 public games and all 183 levels at a perfect RHAE of 100, using 56% fewer actions than first-time human players; with GPT-5.6 Sol it scores 98.27, all without any program synthesis.