5 accepted papers
Multi-robot coarse-to-fine exploration in unknown environments makes great sense in many application fields like search and rescue. For different stages of the task, robots need to extract information from the environment discriminately, which can improve their decision-making capability. To this en
In this paper, we propose a novel deep reinforcement learning (DRL) system for the autonomous navigation of mobile robots that consists of three modules: map navigation, multi-view perception and multi-branch control. Our DRL system takes as the input a routed map provided by a global planner and th…
In a goal-conditioned grasping task, a robot is asked to grasp the objects designated by a user. Existing methods for goal-conditioned grasping either can only handle relatively simple scenes or require extra user annotations. This letter proposes an autonomous method to enable the grasping of targe
Product packing is a typical application in ware-house automation that aims to pick objects from unstructured piles and place them into bins with optimized placing policy. However, it still remains a significant challenge to finish the product packing tasks in general logistics scenarios where the o…
This letter describes a pipeline for autonomous object search using a mobile robot. The robot is required to efficiently find an object in an unknown environment. In this letter, we formulate the object-search problem as a Partially Observable Markov Decision Process (POMDP). The semantic informatio