MTIL: Encoding Full History with Mamba for Temporal Imitation Learning

Standard imitation learning (IL) methods have achieved considerable success in robotics, yet often rely on the Markov assumption, limiting their applicability to tasks where historical context is crucial for disambiguating current observations. This limitation hinders performance in long-horizon sequential manipulation tasks where the correct action depends on past events not fully captured by the current state. To address this fundamental challenge, we introduce Mamba Temporal Imitation Learning (MTIL), a novel approach that leverages the recurrent state dynamics inherent in State Space Models (SSMs), specifically the Mamba architecture. MTIL encodes the entire trajectory history into a compressed hidden state, conditioning action predictions on this comprehensive temporal context alongside current multi-modal observations. Through extensive experiments on simulated benchmarks (ACT dataset tasks, Robomimic, LIBERO) and real-world sequential manipulation tasks specifically designed to probe temporal dependencies, MTIL significantly outperforms state-of-the-art methods like ACT and Diffusion Policy. Our findings affirm the necessity of full temporal context for robust sequential decision-making and validate MTIL as a powerful approach that transcends the inherent limitations of Markovian imitation learning

HDBFormer: Efficient RGB-D Semantic Segmentation with A Heterogeneous Dual-Branch Framework

In RGB-D semantic segmentation for indoor scenes, a key challenge is effectively integrating the rich color information from RGB images with the spatial distance information from depth images. However, most existing methods overlook the inherent differences in how RGB and depth images express information. Properly distinguishing the processing of RGB and depth images is essential to fully exploiting their unique and significant characteristics. To address this, we propose a novel heterogeneous dual-branch framework called HDBFormer, specifically designed to handle these modality differences. For RGB images, which contain rich detail, we employ both a basic and detail encoder to extract local and global features. For the simpler depth images, we propose LDFormer, a lightweight hierarchical encoder that efficiently extracts depth features with fewer parameters. Additionally, we introduce the Modality Information Interaction Module (MIIM), which combines transformers with large kernel convolutions to interact global and local information across modalities efficiently. Extensive experiments show that HDBFormer achieves state-of-the-art performance on the NYUDepthv2 and SUN-RGBD datasets. The code is available at: https://github.com/Weishuobin/HDBFormer.

Large-Scale AI in Telecom: Charting the Roadmap for Innovation, Scalability, and Enhanced Digital Experiences

This white paper discusses the role of large-scale AI in the telecommunications industry, with a specific focus on the potential of generative AI to revolutionize network functions and user experiences, especially in the context of 6G systems. It highlights the development and deployment of Large Telecom Models (LTMs), which are tailored AI models designed to address the complex challenges faced by modern telecom networks. The paper covers a wide range of topics, from the architecture and deployment strategies of LTMs to their applications in network management, resource allocation, and optimization. It also explores the regulatory, ethical, and standardization considerations for LTMs, offering insights into their future integration into telecom infrastructure. The goal is to provide a comprehensive roadmap for the adoption of LTMs to enhance scalability, performance, and user-centric innovation in telecom networks.