This paper proposes a method to estimate normal and shear pressures in prosthetic sockets using sparse sensing and mechanical models. By validating with global wrench and local interface loads, a quasi-static spring-mass contact model with least-squares parameter identification reduces measurement offsets, offering objective fit metrics.

This paper presents DLO-Lab, a differentiable simulator designed for deformable linear objects (e.g., ropes, cables, rubber bands). It models a wide range of material properties, provides a benchmark suite of tasks, and introduces a specialized agent to handle topological complexity and grasp sensitivity. Various policy-learning algorithms are evaluated, with sim-to-real transfer experiments.

This paper proposes a risk-aware model predictive control (RA-MPC) framework that incorporates prediction sets to guarantee risk control below a user-specified threshold without assumptions on the uncertainty distribution. By extending conformal risk control to general spectral risk measures, it achieves distribution-free risk quantification. Simulated vehicle obstacle avoidance scenarios demonstrate improved safety and reduced solve time compared to a baseline RA-MPC framework.