We present Diffuse-CLoC, a guided diffusion framework for physics-based look-ahead control that enables intuitive, steerable, and physically realistic motion generation. While existing kinematics motion generation with diffusion models offer intuitive steering capabilities with inference-time conditioning, they often fail to produce physically viable motions. In contrast, recent diffusion-based control policies have shown promise in generating physically realizable motion sequences, but the lack of kinematics prediction limits their steerability. Diffuse-CLoC addresses these challenges through a key insight: modeling the joint distribution of states and actions within a single diffusion model makes action generation steerable by conditioning it on the predicted states. This approach allows us to leverage established conditioning techniques from kinematic motion generation while producing physically realistic motions. As a result, we achieve planning capabilities without the need for a high-level planner. Our method handles a diverse set of unseen long-horizon downstream tasks through a single pre-trained model, including static and dynamic obstacle avoidance, motion in-betweening, and task-space control. Experimental results show that our method significantly outperforms the traditional hierarchical framework of high-level motion diffusion and low-level tracking.

We design and test a 3 DoF parallel cable system capable of applying precise and accurate impulses to walking and running subjects for the University of Utah's Treadport Active Wind Tunnel (TPAWT). Using Nexus VICON motion capture and gait algorithms, perturbations can be applied at different points in the subject's gait. The use of a PID force controller allow the system to create omnidirectional perturbations with walking and running subjects while having the capability to vary amplitude and direction of perturbations. Analysis is presented of the workspace of the large treadmill to test whether the workspace available to activate these perturbations is safe. This paper reports the efficacy of the system and evaluates how warning a runner before impact may affect their displacement. Participants experienced 48 perturbations while running applied with a random combination of a front/back/left/right impact at either toe-off or mid-stance with or without warning. A two sample T-test reveals that warning a runner before impact significantly reduced the magnitude they were displaced for both toe-off (t(46) = 4.98 p<; .001) and mid-stance (t(46) = 3.44, p = .001).