2 research outputs found
Efficient Hierarchical Robot Motion Planning Under Uncertainty and Hybrid Dynamics
Noisy observations coupled with nonlinear dynamics pose one of the biggest
challenges in robot motion planning. By decomposing nonlinear dynamics into a
discrete set of local dynamics models, hybrid dynamics provide a natural way to
model nonlinear dynamics, especially in systems with sudden discontinuities in
dynamics due to factors such as contacts. We propose a hierarchical POMDP
planner that develops cost-optimized motion plans for hybrid dynamics models.
The hierarchical planner first develops a high-level motion plan to sequence
the local dynamics models to be visited and then converts it into a detailed
continuous state plan. This hierarchical planning approach results in a
decomposition of the POMDP planning problem into smaller sub-parts that can be
solved with significantly lower computational costs. The ability to sequence
the visitation of local dynamics models also provides a powerful way to
leverage the hybrid dynamics to reduce state uncertainty. We evaluate the
proposed planner on a navigation task in the simulated domain and on an
assembly task with a robotic manipulator, showing that our approach can solve
tasks having high observation noise and nonlinear dynamics effectively with
significantly lower computational costs compared to direct planning approaches.Comment: 2nd Conference on Robot Learning (CoRL 2018), Zurich, Switzerlan
Reactive Task and Motion Planning for Robust Whole-Body Dynamic Locomotion in Constrained Environments
Contact-based decision and planning methods are becoming increasingly
important to endow higher levels of autonomy for legged robots. Formal
synthesis methods derived from symbolic systems have great potential for
reasoning about high-level locomotion decisions and achieving complex
maneuvering behaviors with correctness guarantees. This study takes a first
step toward formally devising an architecture composed of task planning and
control of whole-body dynamic locomotion behaviors in constrained and
dynamically changing environments. At the high level, we formulate a two-player
temporal logic game between the multi-limb locomotion planner and its dynamic
environment to synthesize a winning strategy that delivers symbolic locomotion
actions. These locomotion actions satisfy the desired high-level task
specifications expressed in a fragment of temporal logic. Those actions are
sent to a robust finite transition system that synthesizes a locomotion
controller that fulfills state reachability constraints. This controller is
further executed via a low-level motion planner that generates feasible
locomotion trajectories. We construct a set of dynamic locomotion models for
legged robots to serve as a template library for handling diverse environmental
events. We devise a replanning strategy that takes into consideration sudden
environmental changes or large state disturbances to increase the robustness of
the resulting locomotion behaviors. We formally prove the correctness of the
layered locomotion framework guaranteeing a robust implementation by the motion
planning layer. Simulations of reactive locomotion behaviors in diverse
environments indicate that our framework has the potential to serve as a
theoretical foundation for intelligent locomotion behaviors.Comment: 47 pages, 23 figures, 1 tabl