12 accepted papers
Many robot tasks impose constraints on the workspace. For example, a robot may need to move a container without spilling its contents or open a door following the doorknob’s arc. Such constraints may induce narrow volumes in the configuration space, traditionally a challenge for sampling-based metho…
Multiple robots can effectively acquire information from an environment and transmit it to a static base station. However, communication may not be possible everywhere in the environment, so robots must coordinate when and where to rendezvous with disconnected teammates. Such coordination presents c
Using robots to collect data is an effective way to obtain information from the environment and communicate it to a static base station. Furthermore, robots have the capability to communicate with one another, potentially decreasing the time for data to reach the base station. We present a Mixed Int…
Sampling-based motion planning works well in many cases but is less effective if the configuration space has narrow passages. In this paper, we propose a learning-based strategy to sample in these narrow passages, which improves overall planning time. Our algorithm first learns from the configuratio…
Achieving a complete motion planner that guarantees a plan or infeasibility proof in finite time is challenging, especially in high-dimensional spaces. Previous efforts have introduced asymptotically complete motion planners capable of providing a plan or infeasibility proof given long enough time.
Robots that use natural language in collaborative tasks must refer to objects in their environment. Recent work has shown the utility of the linguistic theory of the Givenness Hierarchy (GH) in generating appropriate referring forms. But before referring expression generation, collaborative robots m…
Exploring robots may fail due to environmental hazards. Thus, robots need to account for the possibility of failure to plan the best exploration paths. Optimizing expected utility enables robots to find plans that balance achievable reward with the inherent risks of exploration. Moreover, when robot…
In many exploration scenarios, it is important for robots to efficiently explore new areas and constantly communicate results. Mobile robots inherently couple motion and network topology due to the effects of position on wireless propagation-e.g. distance or obstacles between network nodes. Informat
We present a general approach for constructing proofs of motion planning infeasibility. Effective high-dimensional motion planners, such as sampling-based methods, are limited to probabilistic completeness, so when no plan exists, these planners either do not terminate or can only run until a timeou…
Task and motion planning (TMP) combines discrete search and continuous motion planning. Earlier work has shown that to efficiently find a task-motion plan, the discrete search can leverage information about the continuous geometry. However, incorporating continuous elements into discrete planners pr
We present the first platform-independent evaluation method for task and motion planning (TAMP). Previously point, various problems have been used to test individual planners for specific aspects of TAMP. However, no common set of metrics, formats, and problems have been accepted by the community. W
We present a new algorithm for task and motion planning (TMP) and discuss the requirements and abstrac- tions necessary to obtain robust solutions for TMP in general. Our Iteratively Deepened Task and Motion Planning (IDTMP) method is probabilistically-complete and offers improved per- formance and…