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.
A small Python + C++ library from Kevin Zakka that steps thousands of MuJoCo simulations in parallel on the CPU through a single Batch object: C++ thread pool execution with the GIL released, bind() giving live array views across the whole batch, and expand() for per-simulation model parameters. Memory scales with thread count rather than simulation count, so 4096 simulations stay under 256MB; on a 24-thread machine with a Unitree G1 scene it measured 10.3x over a serial mj_step loop, reaching 676,019 sim-substeps per second with ten substeps batched per call. The repo ships six self-contained solver examples spanning iLQR, predictive-sampling MPC, PPO reinforcement learning, CEM hardware co-design and damped Gauss-Newton system identification, including a Go1 joystick controller that learns to walk in under a minute on a five-year-old M1 laptop.
GRIT is a deployment framework for whole-body motion-tracking control on the Unitree G1 humanoid: ONNX policy, 50 Hz Python inference runtime, MuJoCo sim2sim, live PICO VR full-body teleoperation, and a native SDK2 hardware bridge with stale-command watchdog — a complete low-barrier pipeline for whole-body control and teleoperation data collection.