NavWM is a unified navigation world model that integrates latent world reasoning, multimodal action prediction, and controllable visual generation. By introducing an anchor-based multimodal trajectory forecasting framework, it generates a diverse action space and uses visual foresight for robust closed-loop planning. Experiments show significant improvements in high-fidelity future state generation and zero-shot navigation success.

DynaWM improves bipedal-wheeled robot locomotion on continuous stairs by using a world model regularizer for terrain encoding and a momentum target encoder for stable distillation, enabling smoother and more adaptable movement in simulation and real hardware.

MinInter selects source demonstrations requiring the least interpolation to generate higher-quality synthetic data for imitation learning. Experiments on 12 manipulation tasks show consistent improvements in data generation and policy success rates, with largest gains on contact-rich, long-horizon tasks.

SPACE framework uses Cartesian state delta as a universal action representation, with State Prediction and Adaptive Command Execution to address issues in behavior cloning across robots with different dynamics. Experiments show it outperforms direct command prediction and remains robust under dynamics shifts.

TurboMPC is a differentiable MPC solver that runs entirely on the GPU, supporting state and control inequality constraints, implicit integrators, cross-time-coupled costs, and slack variables. It achieves up to 15× and 58× speedups over state-of-the-art CPU and GPU differentiable solvers, respectively, and scales to planning horizons over 8000 knot points. Deployed on a full-scale car for minimum-time racing, GPU-accelerated Bayesian optimization tuning yields significantly faster driving.