Sensor-based, task-constrained motion generation under uncertainty

Abstract

Abstract-Mobile manipulation targets applications in dynamic and unstructured environments. Motion generation methods suitable for these applications must account for endeffector task constraints, must reason about environment uncertainty, i.e. the fact that the exact state of the dynamic environment cannot be known to the robot, and should do so only using their on-board sensors. We present the ExpectedShortest-Path Elastic Roadmap (ESPER) planner as a motion generation method suitable for mobile manipulation. It integrates task-constrained, whole-body, reactive motion generation in high-dimensional configuration space with reasoning about uncertainty. In our experiments, we generate task-consistent motion in uncertain environments on a real-world mobile manipulator only relying on on-board sensors.

SLAP: Simultaneous Localization and Planning Under Uncertainty via Dynamic Replanning in Belief Space

“…Application of POMDPs to physical robots: From an experimental point of view, a few recent works have focused on applying belief space planning to real-world robots. [41] implements a belief planner on a mobile manipulator with time of traversal as a cost metric. [42] is an integrated task and motion planner, utilizing symbolic abstraction, whose performance is demonstrated on a PR2 robot tasked with picking and placing household objects.…”