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. The code and Reinforcement Learning environments will be made available on the project webpage *.

Authors

• Lakshadeep Naik
• Sinan Kalkan
• Sune Lundø Sørensen
• Mikkel Baun Kjærgaard
• Norbert Krüger

Keywords

• Learning systems
• Navigation
• Grasping
• Reinforcement learning
• Manipulators
• Graph neural networks
• Computational efficiency
• Planning
• Optimization
• Intelligent robots
• Mobile Manipulator
• Estimation Method
• Optimal Combination
• Learning-based Methods
• Exact Method
• Learning-based Approaches
• Suboptimal Solution
• Objects In The Scene
• Combinatorial Optimization Problem
• State Space
• Maximum Velocity
• Multilayer Perceptron
• Dynamic Programming
• Number Of Objects
• Optimal Sequence
• ReLU Activation
• Pose Estimation
• Planning Time
• Pose Of Frame
• Total Execution Time
• Object Pose
• Quick Solution
• Contextual Embedding
• Routing Problem
• Object In Frame
• Configuration Of Objects
• Types Of Nodes