Cumulative Training and Transfer Learning for MultiRobots CollisionFree Navigation Problems Recently, the characteristics of robot autonomy, decentralized control, collective decisionmaking ability, high fault tolerance, etc. have significantly increased the applications of swarm robotics in targeted material delivery, precision farming, surveillance, defense and many other areas. In these multiagent systems, safe collision avoidance is one of the most fundamental and important problems. Difference approaches, especially reinforcement learning, have been applied to solve this problem. This paper introduces a new cumulative learning approach which comprises of application of transfer learning with distributed multiagent reinforcement learning techniques to solve collisionfree navigation for swarm robotics. In our method, throughout the learning processes from the least complexity scenario to the most complex one, multiple agents can improve the shared policy through parameter sharing, reward shaping and multiround multisteps learning. We have adapted two policy gradient algorithms (TRPO and PPO) as the core of our distributed multiagent reinforcement learning method. The performance has shown that our new methodology can help reduce the training time and generate a robust navigation plan that can easily be generalized to complex indoor scenarios.

Learning Safe Unlabeled MultiRobot Planning with Motion Constraints In this paper, we present a learning approach to goal assignment and trajectory planning for unlabeled robots operating in 2D, obstaclefilled workspaces. More specifically, we tackle the unlabeled multirobot motion planning problem with motion constraints as a multiagent reinforcement learning problem with some sparse global reward. In contrast with previous works, which formulate an entirely new handcrafted optimization cost or trajectory generation algorithm for a different robot dynamic model, our framework is a general approach that is applicable to arbitrary robot models. Further, by using the velocity obstacle, we devise a smooth projection that guarantees collision free trajectories for all robots with respect to their neighbors and obstacles. The efficacy of our algorithm is demonstrated through varied simulations. A video describing our method and results can be found here.