  Loco-manipulation planning skills are pivotal for expanding the utility of
robots in everyday environments. These skills can be assessed based on a
system's ability to coordinate complex holistic movements and multiple contact
interactions when solving different tasks. However, existing approaches have
been merely able to shape such behaviors with hand-crafted state machines,
densely engineered rewards, or pre-recorded expert demonstrations. Here, we
propose a minimally-guided framework that automatically discovers whole-body
trajectories jointly with contact schedules for solving general
loco-manipulation tasks in pre-modeled environments. The key insight is that
multi-modal problems of this nature can be formulated and treated within the
context of integrated Task and Motion Planning (TAMP). An effective bilevel
search strategy is achieved by incorporating domain-specific rules and
adequately combining the strengths of different planning techniques: trajectory
optimization and informed graph search coupled with sampling-based planning. We
showcase emergent behaviors for a quadrupedal mobile manipulator exploiting
both prehensile and non-prehensile interactions to perform real-world tasks
such as opening/closing heavy dishwashers and traversing spring-loaded doors.
These behaviors are also deployed on the real system using a two-layer
whole-body tracking controller.
