quackd connects the Microduck biped to any LLM: state goals in plain language, the LLM orchestrates the robot built-in skills step by step, enforced by .duck task contracts, with an MCP server and multi-robot flock mode.
A compact generalist navigation model from Light Origins: Qwen3-VL-4B backbone + dual-channel pointing + RVQ action tokens. One checkpoint covers instruction following, object navigation and visual tracking, transferring zero-shot across humanoid/quadruped/wheeled/aerial robots. Trained entirely in simulation, Apache-2.0.
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.
ElectroPup is a 4.5 kg, 12-DoF DIY quadruped: twelve MG4010E-i10v3 CAN BLDC actuators split into four per-leg CAN segments, a Raspberry Pi 5 for compute, and a single ~7,700-line pure-Python service with no ROS in the stack. The repository publishes OnShape CAD, two KiCad v8 boards, a bill of materials, measured weights and power draw (21.6 W suspended, 33.8 W standing), plus MuJoCo simulation, a React Native/Expo browser UI, four Bezier-foot gaits, and a full build guide. The author also documents known gaps: no license file, no contact sensing, and a simulation model without approximate masses or inertias.
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.
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.
Open-source hand exoskeleton: 12 joint encoders, 8 tendon-driven motors, on-device Teensy control, and synchronized capture/EMG for motion capture, teleoperation and haptics research.
An engine-native Unity architecture: hand-tuned PD balance controller plus raycast-triggered per-foot IK with ConfigurableJoint physics — physically simulated ragdoll biped balance and locomotion without mocap, offline optimization, or RL, with control-theory derivation and an evaluation protocol.