We tackle monocular 6D pose estimation for small and weakly textured objects using a Transformer enhanced with geometry-aware priors. Low-Rank Gated Module applies a low rank bottleneck with a learned gate to denoise query features and to amplify fine detail. Topology Optimization builds a Gaussian affinity graph over queries, pools a global summary, and broadcasts it back while enforcing a top‑k connectivity loss that promotes coherent clusters. A training-time Riemannian Refinement performs a few geodesic steps on SO(3) with learned step sizes and per query weights, keeping rotations on the manifold and stabilizing translation. Our methods achieved excellent results on three publicly available datasets, and we propose a new small objects 6DoF estimation dataset for UAVs.

Understanding how humans cooperatively utilize semantic knowledge to explore unfamiliar environments and decide on navigation directions is critical for house service multi-robot systems. Previous methods primarily focused on single-robot centralized planning strategies, which severely limited exploration efficiency. Recent research has considered decentralized planning strategies for multiple robots, assigning separate planning models to each robot, but these approaches often overlook communication costs. In this work, we propose Multimodal Chain-of-Thought Co-Navigation (MCoCoNav), a modular approach that utilizes multimodal Chain-of-Thought to plan collaborative semantic navigation for multiple robots. MCoCoNav combines visual perception with Vision Language Models (VLMs) to evaluate exploration value through probabilistic scoring, thus reducing time costs and achieving stable outputs. Additionally, a global semantic map is used as a communication bridge, minimizing communication overhead while integrating observational results. Guided by scores that reflect exploration trends, robots utilize this map to assess whether to explore new frontier points or revisit history nodes. Experiments on HM3D_v0.2 and MP3D demonstrate the effectiveness of our approach.

Instance ImageGoal Navigation (IIN) entails an agent autonomously seeking out a specific object instance depicted by a goal image in an unknown environment. While Large Language Models (LLMs) have shown promise in navigation tasks similar to IIN, their application to IIN remains unexplored. Furthermore, existing LLM-based exploration faces challenges such as inaccurate reasoning due to informative environmental information available to the agent, especially in the early episode stages, and the inability of reinforcement learning(RL) exploration to fully leverage gathered information. Moreover, previous IIN methods did not productively verify potentially distant goal objects discovered during exploration. This work proposes MoPE–Mixture of Policy Experts for exploration and potential goal verification with multimodal information when exploring. Specifically, the hybrid exploration policy comprises an LLM and an RL-based Policy Network (RLPN) to generate an exploration goal to explore efficiently. Our MoPE model surpasses prior approaches on the HM3D datasets significantly.

Intelligent Transportation System (ITS) applications in vehicle networks often use latency, reliability, and throughput as performance metrics. However, these traditional metrics fail to indicate the freshness of data. ”Age-of-information” (AoI) has been emphasized as a critical requirement for real-time applications as a new performance metric. Traditional broadcast-based data propagation tends to cause congestion because it needs a lot of vehicles to participate in the data transmission, resulting in large AoI. With the help of mobile edge computing, this paper proposes a new unicast-based data propagation approach to reduce AoI. In this scenario, an edge computing server computes and determines vehicle nodes for the purpose of propagating time-sensitive data via V2V communication. The goal of this research is to provide an AoI-aware unicast data propagation method that optimizes the initial vehicle nodes selection and design scheduling rules for maintaining information fresh under a coverage rate restriction (corresponding to emergency level). Simulation results show that the proposed unicast-based method has lower AAoI and PAoI than that of the traditional broadcast-based method.