4 accepted papers
In Multi-Policy Decision-Making (MPDM), many computationally-expensive forward simulations are performed in order to predict the performance of a set of candidate policies. In risk-aware formulations of MPDM, only the worst outcomes affect the decision making process, and efficiently finding these i…
Risk-aware Multi-Policy Decision Making (MPDM)is a powerful framework for reliable navigation in a dynamic social environment where rather than evaluating individual trajectories, a “library” of policies (reactive controllers)is evaluated by anticipating potentially dangerous future outcomes using a…
In the Multi-Policy Decision Making (MPDM) framework, a robot's policy is elected by sampling from the distribution of current states, predicting future outcomes through forward simulation, and selecting the policy with the best expected performance. Electing the best plan depends on sampling initia…
In dynamic environments crowded with people, robot motion planning becomes difficult due to the complex and tightly-coupled interactions between agents. Trajectory planning methods, supported by models of typical human behavior and personal space, often produce reasonable behavior. However, they do…