Learning Constrained Distributions of Robot Configurations with Generative Adversarial Network

In high dimensional robotic system, the manifold of the valid configuration space often has a complex shape, especially under constraints such as end-effector orientation or static stability. We propose a generative adversarial network approach to learn the distribution of valid robot configurations under such constraints. It can generate configurations that are close to the constraint manifold. We present two applications of this method. First, by learning the conditional distribution with respect to the desired end-effector position, we can do fast inverse kinematics even for very high degrees of freedom (DoF) systems. Then, we use it to generate samples in sampling-based constrained motion planning algorithms to reduce the necessary projection steps, speeding up the computation. We validate the approach in simulation using the 7-DoF Panda manipulator and the 28-DoF humanoid robot Talos

Kinematic transfer learning of sampling distributions for manipulator motion planning

Recent research has shown that guiding sampling-based planners with sampling distributions, learned from previous experiences via density estimation, can significantly decrease computation times for motion planning. We propose an algorithm that can estimate the density from the experiences of a robot with different kinematic structure, on the same task. The method allows to generalize collected data from one source manipulator to similarly designed target manipulators, significantly reducing the computation time for new queries for the target manipulator. We evaluate the algorithm in two experiments, including a constrained manipulation task with five different collaborative robots, and show that transferring information can significantly decrease planning time

Repetition Sampling for Efficiently Planning Similar Constrained Manipulation Tasks

We present repetition sampling, a new adaptive strategy for sampling based planning, which extracts information from previous solutions to focus the search for a similar task on relevant configuration space. We show how to generate distributions for repetition sampling by learning Gaussian Mixture Models from prior solutions. We present how to bias a sampling based planner with the learned distribution to generate new paths for similar tasks. We illustrate our method in a simple maze which explains the generation of the distribution and how repetition sampling can generalize over different environments. We show how to apply repetition sampling to similar constrained manipulation tasks and present our results including significant speedup in execution time when compared to uniform sampling

Asymptotically Optimal Sampling-Based Motion Planning Methods

Motion planning is a fundamental problem in autonomous robotics that requires finding a path to a specified goal that avoids obstacles and takes into account a robot's limitations and constraints. It is often desirable for this path to also optimize a cost function, such as path length. Formal path-quality guarantees for continuously valued search spaces are an active area of research interest. Recent results have proven that some sampling-based planning methods probabilistically converge toward the optimal solution as computational effort approaches infinity. This survey summarizes the assumptions behind these popular asymptotically optimal techniques and provides an introduction to the significant ongoing research on this topic.Comment: Posted with permission from the Annual Review of Control, Robotics, and Autonomous Systems, Volume 4. Copyright 2021 by Annual Reviews, https://www.annualreviews.org/. 25 pages. 2 figure