6 accepted papers
Rearranging disarrayed objects to their intended goal states requires the agent to comprehend the changes that have occurred in the scene and to reason about the process of these changes. To address this, we propose a novel perspective on the visual rearrangement task, drawing inspiration from the d
Deep learning-based malicious traffic detection requires large-scale, accurately labeled datasets. However, realistic malicious traffic datasets contain noisy labels due to annotation errors, which can degrade the performance of detection models. Existing methods for handling noisy labels involve ro…
Towards visual room rearrangement for embodied agents, this paper tackles the intricate challenge of restoring a disarrayed scene configuration to its intended goal state. The task necessitates a range of sophisticated capabilities, including efficient spatial navigation, precise and accurate object…
This paper presents a novel category agnostic model for visual rearrangement task which can help an embodied agent to physically recover the shuffled scene configuration without any category concepts to the goal configuration. Previous methods usually follow a similar architecture completing the rea…
Visual navigation is to let the agent reach the target according to the continuous visual input. In most previous works visual navigation is usually assumed to be done in a static and ideal environment: the target is always reachable with no need to alter the environment. However the "messy" environ…
This paper presents a nonlinear control approach for quadrotor Micro Aerial Vehicles (MAVs), which combines a backstepping-like regulator based on the solution of a certain class of global output regulation problems for the rigid body equations on SO(3), a robust controller for the system with bound…