skip to main content

Search for: All records

Award ID contains: 1849348

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Free, publicly-accessible full text available January 1, 2023
  2. Proving motion planning infeasibility is an important part of a complete motion planner. Common approaches for high-dimensional motion planning are only probabilistically complete. Previously, we presented an algorithm to construct infeasibility proofs by applying machine learning to sampled configurations from a bidirectional sampling-based planner. In this work, we prove that the learned manifold converges to an infeasibility proof exponentially. Combining prior approaches for sampling-based planning and our converging infeasibility proofs, we propose the term asymptotic completeness to describe the property of returning a plan or infeasibility proof in the limit. We compare the empirical convergence of different sampling strategies tomore »validate our analysis.« less
    Free, publicly-accessible full text available January 1, 2023
  3. Free, publicly-accessible full text available September 27, 2022
  4. Shell, Dylan A ; Toussaint, Marc (Ed.)
    We present a learning-based approach to prove infeasibility of kinematic motion planning problems. Sampling-based motion planners are effective in high-dimensional spaces but are only probabilistically complete. Consequently, these planners cannot provide a definite answer if no plan exists, which is important for high-level scenarios, such as task-motion planning. We propose a combination of bidirectional sampling-based planning (such as RRT-connect) and machine learning to construct an infeasibility proof alongside the two search trees. An infeasibility proof is a closed manifold in the obstacle region of the configuration space that separates the start and goal into disconnected components of the free configurationmore »space. We train the manifold using common machine learning techniques and then triangulate the manifold into a polytope to prove containment in the obstacle region. Under assumptions about learning hyper-parameters and robustness of configuration space optimization, the output is either an infeasibility proof or a motion plan. We demonstrate proof construction for 3-DOF and 4-DOF manipulators and show improvement over a previous algorithm.« less
  5. Most previous work on enabling robots’ moral competence has used norm-based systems of moral reasoning. However, a number of limitations to norm-based ethical theories have been widely acknowledged. These limitations may be addressed by role-based ethical theories, which have been extensively discussed in the philosophy of technology literature but have received little attention within robotics. My work proposes a hybrid role/norm-based model of robot cognitive processes including moral cognition.
  6. Ang, Marcelo H. ; Khatib, Oussama ; Siciliano, Bruno ; Kavraki, Lydia E (Ed.)
    Task and motion planning operates in a combined discrete and continuous space to find a sequence of high-level, discrete actions and corresponding low-level, continuous paths to go from an initial state to a goal state.