Feedback Motion Planning for Stochastic Nonlinear Systems with Signal Temporal Logic Specifications

We study feedback motion planning for continuous-time stochastic nonlinear systems under signal temporal logic (STL) specifications. We propose a framework that synthesizes control policies for chance-constrained STL trajectory optimization problems, with the goal of ensuring that the closed-loop stochastic system satisfies a given STL formula with high probability (e.g., 99.99\%). Our approach is based on a predicate erosion strategy that transforms the intractable stochastic problem into a deterministic STL trajectory optimization problem with tightened STL formula constraints. The amount of erosion is determined by a probabilistic reachable tube (PRT) that bounds the deviation between the stochastic trajectory and an associated nominal trajectory. To compute such bounds, we leverage contraction theory and feedback design, and develop several tracking controllers. This yields a complete feedback motion planning pipeline which can be implemented by numerical optimizations. We demonstrate the efficacy and versatility of the proposed framework through simulations on several robotic systems and through experiments on a real-world quadrupedal robot, and show that it is less conservative and achieves higher specification satisfaction probability than representative baselines.

Computer-Controlled 3D Freeform Surface Weaving

In this paper, we present a new computer-controlled weaving technology that enables the fabrication of woven structures in the shape of given 3D surfaces by using threads in non-traditional materials with high bending-stiffness, allowing for multiple applications with the resultant woven fabrics. A new weaving machine and a new manufacturing process are developed to realize the function of 3D surface weaving by the principle of short-row shaping. A computational solution is investigated to convert input 3D freeform surfaces into the corresponding weaving operations (indicated as W-code) to guide the operation of this system. A variety of examples using cotton threads, conductive threads and optical fibres are fabricated by our prototype system to demonstrate its functionality.

Online Control for Linear Dynamics: A Data-Driven Approach

This paper considers an online control problem over a linear time-invariant system with unknown dynamics, bounded disturbance, and adversarial cost. We propose a data-driven strategy to reduce the regret of the controller. Unlike model-based methods, our algorithm does not identify the system model, instead, it leverages a single noise-free trajectory to calculate the accumulation of disturbance and makes decisions using the accumulated disturbance action controller we design, whose parameters are updated by online gradient descent. We prove that the regret of our algorithm is $\mathcal{O}(\sqrt{T})$ under mild assumptions, suggesting that its performance is on par with model-based methods.