Safe navigation of cluttered environments is a critical challenge in robotics. It is typically approached by separating the planning and tracking problems, with planning executed on a reduced order model to generate reference trajectories, and control techniques used to track these trajectories on the full order dynamics. Inevitable tracking error necessitates robustification of the nominal plan to ensure safety; in many cases, this is accomplished via worst-case bounding, which ignores the fact that some trajectories of the planning model may be easier to track than others. In this work, we present a novel method leveraging massively parallel simulation to learn a dynamic tube representation, which characterizes tracking performance as a function of actions taken by the planning model. Planning model trajectories are then optimized such that the dynamic tube lies in the free space, allowing a balance between performance and safety to be traded off in real time. The resulting Dynamic Tube MPC is applied to the 3D hopping robot ARCHER, enabling agile and performant navigation of cluttered environments, and safe collision-free traversal of narrow corridors.

Accurate walking speed estimation in lower-limb prostheses is crucial for delivering biomechanically appropriate assistance across varying speeds. However, training robust models requires extensive domain-specific, user-dependent (DEP) data, which is impractical for every new prosthesis user. This study presents a transfer learning framework to simplify and enhance the training process. Convolutional neural networks were pre-trained on publicly available datasets from able-bodied (AB) individuals and transfemoral amputees using the Open Source Leg (OSL) knee-ankle prosthesis, then fine-tuned with data from a transfemoral amputee using the Power Knee (PK) prosthesis. The fine-tuned models, AB-PK and OSL-PK were trained with varying data amounts and evaluated across constant and variable walking speed trials, with performance compared to DEP models trained from scratch on PK data. Training and testing were conducted on a per-subject basis, with performance averaged across subjects (N=7). The lowest post-fine-tuning error was observed in AB-PK, with RMSE values of 0.041 m/s for constant speeds, 0.072 m/s for variable speeds, and 0.088 m/s for novel speeds not included in the original training data. Significant error reductions were observed in both fine-tuned models compared to DEP when fewer than 30 gait cycles per speed of training data were available. Notably, AB datasets appeared highly viable for this application and may even outperform OSL datasets in transfer learning for walking speed estimation, perhaps due to the much larger original training dataset. This approach highlights the potential of transfer learning across different subject populations and devices, offering insights into the data needed to achieve state-of-the-art speed estimation.