  ReachBot, a proposed robotic platform, employs extendable booms as limbs for
mobility in challenging environments, such as martian caves. When attached to
the environment, ReachBot acts as a parallel robot, with reconfiguration driven
by the ability to detach and re-place the booms. This ability enables
manipulation-focused scientific objectives: for instance, through operating
tools, or handling and transporting samples. To achieve these capabilities, we
develop a two-part solution, optimizing for robustness against task uncertainty
and stochastic failure modes. First, we present a mixed-integer stance planner
to determine the positioning of ReachBot's booms to maximize the task wrench
space about the nominal point(s). Second, we present a convex tension planner
to determine boom tensions for the desired task wrenches, accounting for the
probabilistic nature of microspine grasping. We demonstrate improvements in key
robustness metrics from the field of dexterous manipulation, and show a large
increase in the volume of the manipulation workspace. Finally, we employ
Monte-Carlo simulation to validate the robustness of these methods,
demonstrating good performance across a range of randomized tasks and
environments, and generalization to cable-driven morphologies. We make our code
available at our project webpage,
https://stanfordasl.github.io/reachbot_manipulation/
