9 accepted papers
While scaling laws for imitation learning have primarily focused on generalization in open-world settings, the relationship between data and precision in closed-world tasks like robotic assembly remains largely unexplored. This paper systematically investigates this relationship and introduces a nov…
Accurate and real-time stereo depth estimation is important for autonomous robots, such as autonomous aerial vehicles (AAVs). Due to the computation constraints of these miniaturized robots, current state-of-the-art algorithms deploy light-weight neural networks while using self-supervised online ad
Robots often need to accomplish complex tasks in unknown environments, which is a challenging problem, involving autonomous exploration for acquiring necessary scene knowledge and task planning. In traditional approaches, the agent first explores the environment to instantiate a complete planning do
Fig. 5b in [1] shows the geometric center of vertices in robots after map merging, but it shows the situation before map merging. The correct figure of Fig. 5 in [1] is shown in Fig. 1
Autonomous exploration and mapping of unknown terrains employing single or multiple robots is an essential task in mobile robotics and has therefore been widely investigated. Nevertheless, given the lack of unified data sets, metrics, and platforms to evaluate the exploration approaches, we develop…
Collaborative perception in unknown environments is a critical task for multi-robot systems. Without external positioning, multi-robot mapping systems have relied on the transfer of place recognition (PR) descriptors or sensor data for the relative pose estimation (RelPose) and share their local map
Multi-robot exploration in unknown environments is a fundamental task for a multi-robot system, involving inter-robot communication through messages among the robots. However, in a restricted communication environment, the limited communication resources become the system's bottleneck due to a large
Collaborative exploration in an unknown environment without external positioning under limited communication is an essential task for multi-robot applications. For inter-robot positioning, various Distributed Simultaneous Localization and Mapping (DSLAM) systems share the Place Recognition (PR) desc…
We introduce an automatic curriculum algorithm, Variational Automatic Curriculum Learning (VACL), for solving challenging goal-conditioned cooperative multi-agent reinforcement learning problems. We motivate our curriculum learning paradigm through a variational perspective, where the learning objec…