BaSeNet: A Learning-based Mobile Manipulator Base Pose Sequence Planning for Pickup Tasks

In many applications, a mobile manipulator robot is required to grasp a set of objects distributed in space. This may not be feasible from a single base pose and the robot must plan the sequence of base poses for grasping all objects, minimizing the total navigation and grasping time. This is a Combinatorial Optimization problem that can be solved using exact methods, which provide optimal solutions but are computationally expensive, or approximate methods, which offer computationally efficient but sub-optimal solutions. Recent studies have shown that learning-based methods can solve Combinatorial Optimization problems, providing near-optimal and computationally efficient solutions. In this work, we present BASENET - a learning-based approach to plan the sequence of base poses for the robot to grasp all the objects in the scene. We propose a Reinforcement Learning based solution that learns the base poses for grasping individual objects and the sequence in which the objects should be grasped to minimize the total navigation and grasping costs using Layered Learning. As the problem has a varying number of states and actions, we represent states and actions as a graph and use Graph Neural Networks for learning. We show that the proposed method can produce comparable solutions to exact and approximate methods with significantly less computation time.

Robotic Task Success Evaluation Under Multi-modal Non-Parametric Object Pose Uncertainty

Accurate 6D object pose estimation is essential for various robotic tasks. Uncertain pose estimates can lead to task failures; however, a certain degree of error in the pose estimates is often acceptable. Hence, by quantifying errors in the object pose estimate and acceptable errors for task success, robots can make informed decisions. This is a challenging problem as both the object pose uncertainty and acceptable error for the robotic task are often multi-modal and cannot be parameterized with commonly used uni-modal distributions. In this paper, we introduce a framework for evaluating robotic task success under object pose uncertainty, representing both the estimated error space of the object pose and the acceptable error space for task success using multi-modal non-parametric probability distributions. The proposed framework pre-computes the acceptable error space for task success using dynamic simulations and subsequently integrates the pre-computed acceptable error space over the estimated error space of the object pose to predict the likelihood of the task success. We evaluated the proposed framework on two mobile manipulation tasks. Our results show that by representing the estimated and the acceptable error space using multi-modal non-parametric distributions, we achieve higher task success rates and fewer failures.