Scaling Sampling-based Motion Planning to Humanoid Robots

TL;DR

This paper introduces a scalable sampling-based motion planning method for humanoid robots that uses customized state spaces and biased sampling, enabling efficient trajectory planning without offline computation.

Contribution

It presents a novel approach that adapts sampling-based algorithms to humanoids using specialized representations and strategies, facilitating transfer to new platforms without precomputation.

Findings

Successfully planned motions for NASA Valkyrie humanoid

Benchmarking identified suitable algorithms for humanoid motion planning

Method generates valid, executable plans in complex environments

Abstract

Planning balanced and collision-free motion for humanoid robots is non-trivial, especially when they are operated in complex environments, such as reaching targets behind obstacles or through narrow passages. We propose a method that allows us to apply existing sampling--based algorithms to plan trajectories for humanoids by utilizing a customized state space representation, biased sampling strategies, and a steering function based on a robust inverse kinematics solver. Our approach requires no prior offline computation, thus one can easily transfer the work to new robot platforms. We tested the proposed method solving practical reaching tasks on a 38 degrees-of-freedom humanoid robot, NASA Valkyrie, showing that our method is able to generate valid motion plans that can be executed on advanced full-size humanoid robots. We also present a benchmark between different motion planning algorithms evaluated on a variety of reaching motion problems. This allows us to find suitable algorithms for solving humanoid motion planning problems, and to identify the limitations of these algorithms.