5 accepted papers
We demonstrate the surprising real-world effectiveness of a very simple approach to whole-body model- predictive control (MPC) of quadruped and humanoid robots: the iterative linear-quadratic regulator (iLQR) algorithm with MuJoCo dynamics and finite-difference approximated derivatives. Building upo…
Humanoid robots hold the potential for unparalleled versatility by performing human-like, whole-body skills. However, achieving agile and coordinated whole-body motions remains a significant challenge due to the dynamics mismatch between simulation and real-world physics. Existing approaches, such a…
Due to high dimensionality and non-convexity, real-time optimal control using full-order dynamics models for legged robots is challenging. Therefore, Nonlinear Model Predictive Control (NMPC) approaches are often limited to reduced-order models or local approximations. Sampling-based MPC has shown p
Recent advances in diffusion models have demonstrated their strong capabilities in generating high-fidelity samples from complex distributions through an iterative refinement process. Despite the empirical success of diffusion models in motion planning and control, the model-free nature of these met…
Learning to move is a primary goal for animals and robots, where ensuring safety is often important when optimizing control policies on the embodied systems. For complex tasks such as the control of human or humanoid control, the high-dimensional parameter space adds complexity to the safe optimizat…