The field of autonomous robotics is moving towards more sophisticated path planning methods that take into account risk and uncertainty. Researchers are developing innovative approaches to address the challenges of navigating complex and dynamic environments. These approaches include risk-averse traversal of graphs, dynamic bipedal motion planning, and coverage path planning for unknown environments. Notable papers in this area include those that propose novel algorithms for risk-aware motion planning, such as the use of conditional value-at-risk (CVaR) criteria and cascaded diffusion models. Noteworthy papers in this area are: Risk-Averse Traversal of Graphs with Stochastic and Correlated Edge Costs for Safe Global Planetary Mobility, which proposes a novel search algorithm for finding exact CVaR-optimal policies. Cascaded Diffusion Models for Neural Motion Planning, which presents a approach for learning global motion planning using diffusion policies.