9 accepted papers
The rapid growth of video-text data presents challenges in storage and computation during training. Online learning, which processes streaming data in real-time, offers a promising solution to these issues while also allowing swift adaptations in scenarios demanding real-time responsiveness. One str…
As with many other problems, real-world regression is plagued by the presence of noisy labels, an inevitable issue that demands our attention. Fortunately, much real-world data often exhibits an intrinsic property of continuously ordered correlations between labels and features, where data points w…
In active source seeking, a robot takes repeated measurements in order to locate a signal source in a cluttered and unknown environment. A key component of an active source seeking robot planner is a model that can produce estimates of the signal at unknown locations with uncertainty quantification.…
Robust mission planning is an essential component for mission autonomy to perform complicated tasks in extreme environments. In this paper, we are interested in the role of semantic abstractions for guiding autonomous mission planning. In particular, we focus on how semantics can be leveraged to tra…
Continually learning in the real world must overcome many challenges, among which noisy labels are a common and inevitable issue. In this work, we present a replay-based continual learning framework that simultaneously addresses both catastrophic forgetting and noisy labels for the first time. Our s…
This paper presents a distributed optimization framework and its local utility design for communication-aware information gathering by mobile robotic sensor networks. The main idea of the optimization is that each robot decides based on its local utility that considers the decisions of other neighbo…
This letter investigates the computation of information measures for distributed communication-aware information gathering by robotic sensor networks. The mutual information between an unknown target state and measurements received over a lossy network is derived in order to combine sensing and comm
Many real-world reinforcement learning tasks require multiple agents to make sequential decisions under the agents’ interaction, where well-coordinated actions among the agents are crucial to achieve the target goal better at these tasks. One way to accelerate the coordination effect is to enable mu…
This paper examines the problem of distributed path planning for a mobile sensor network comprised of communication-aware robots performing general information gathering missions. Mutual information is derived for distributed sensing over packet erasure channels that model multi-hop communication. W…