Phoenix: A Motion-based Self-Reflection Framework for Fine-grained Robotic Action Correction

Building a generalizable self-correction system is crucial for robots to recover from failures. Despite advancements in Multimodal Large Language Models (MLLMs) that empower robots with semantic reflection ability for failure, translating semantic reflection into how to correct fine-grained robotic actions remains a significant challenge. To address this gap, we build the Phoenix framework, which leverages motion instruction as a bridge to connect high-level semantic reflection with low-level robotic action correction. In this motion-based self-reflection framework, we start with a dual-process motion adjustment mechanism with MLLMs to translate the semantic reflection into coarse-grained motion instruction adjustment. To leverage this motion instruction for guiding how to correct fine-grained robotic actions, a multi-task motion-conditioned diffusion policy is proposed to integrate visual observations for high-frequency robotic action correction. By combining these two models, we could shift the demand for generalization capability from the low-level manipulation policy to the MLLMs-driven motion adjustment model and facilitate precise, fine-grained robotic action correction. Utilizing this framework, we further develop a lifelong learning method to automatically improve the model's capability from interactions with dynamic environments. The experiments conducted in both the RoboMimic simulation and real-world scenarios prove the superior generalization and robustness of our framework across a variety of manipulation tasks. Our code is released at \href{https://github.com/GeWu-Lab/Motion-based-Self-Reflection-Framework}{https://github.com/GeWu-Lab/Motion-based-Self-Reflection-Framework}.

CPG-EVAL: A Multi-Tiered Benchmark for Evaluating the Chinese Pedagogical Grammar Competence of Large Language Models

Purpose: The rapid emergence of large language models (LLMs) such as ChatGPT has significantly impacted foreign language education, yet their pedagogical grammar competence remains under-assessed. This paper introduces CPG-EVAL, the first dedicated benchmark specifically designed to evaluate LLMs' knowledge of pedagogical grammar within the context of foreign language instruction. Methodology: The benchmark comprises five tasks designed to assess grammar recognition, fine-grained grammatical distinction, categorical discrimination, and resistance to linguistic interference. Findings: Smaller-scale models can succeed in single language instance tasks, but struggle with multiple instance tasks and interference from confusing instances. Larger-scale models show better resistance to interference but still have significant room for accuracy improvement. The evaluation indicates the need for better instructional alignment and more rigorous benchmarks, to effectively guide the deployment of LLMs in educational contexts. Value: This study offers the first specialized, theory-driven, multi-tiered benchmark framework for systematically evaluating LLMs' pedagogical grammar competence in Chinese language teaching contexts. CPG-EVAL not only provides empirical insights for educators, policymakers, and model developers to better gauge AI's current abilities in educational settings, but also lays the groundwork for future research on improving model alignment, enhancing educational suitability, and ensuring informed decision-making concerning LLM integration in foreign language instruction.

Doppler-SLAM: Doppler-Aided Radar-Inertial and LiDAR-Inertial Simultaneous Localization and Mapping

Simultaneous localization and mapping (SLAM) is a critical capability for autonomous systems. Traditional SLAM approaches, which often rely on visual or LiDAR sensors, face significant challenges in adverse conditions such as low light or featureless environments. To overcome these limitations, we propose a novel Doppler-aided radar-inertial and LiDAR-inertial SLAM framework that leverages the complementary strengths of 4D radar, FMCW LiDAR, and inertial measurement units. Our system integrates Doppler velocity measurements and spatial data into a tightly-coupled front-end and graph optimization back-end to provide enhanced ego velocity estimation, accurate odometry, and robust mapping. We also introduce a Doppler-based scan-matching technique to improve front-end odometry in dynamic environments. In addition, our framework incorporates an innovative online extrinsic calibration mechanism, utilizing Doppler velocity and loop closure to dynamically maintain sensor alignment. Extensive evaluations on both public and proprietary datasets show that our system significantly outperforms state-of-the-art radar-SLAM and LiDAR-SLAM frameworks in terms of accuracy and robustness. To encourage further research, the code of our Doppler-SLAM and our dataset are available at: https://github.com/Wayne-DWA/Doppler-SLAM.

AerialVG: A Challenging Benchmark for Aerial Visual Grounding by Exploring Positional Relations

Visual grounding (VG) aims to localize target objects in an image based on natural language descriptions. In this paper, we propose AerialVG, a new task focusing on visual grounding from aerial views. Compared to traditional VG, AerialVG poses new challenges, \emph{e.g.}, appearance-based grounding is insufficient to distinguish among multiple visually similar objects, and positional relations should be emphasized. Besides, existing VG models struggle when applied to aerial imagery, where high-resolution images cause significant difficulties. To address these challenges, we introduce the first AerialVG dataset, consisting of 5K real-world aerial images, 50K manually annotated descriptions, and 103K objects. Particularly, each annotation in AerialVG dataset contains multiple target objects annotated with relative spatial relations, requiring models to perform comprehensive spatial reasoning. Furthermore, we propose an innovative model especially for the AerialVG task, where a Hierarchical Cross-Attention is devised to focus on target regions, and a Relation-Aware Grounding module is designed to infer positional relations. Experimental results validate the effectiveness of our dataset and method, highlighting the importance of spatial reasoning in aerial visual grounding. The code and dataset will be released.

Knowledge Graph Completion with Relation-Aware Anchor Enhancement

Text-based knowledge graph completion methods take advantage of pre-trained language models (PLM) to enhance intrinsic semantic connections of raw triplets with detailed text descriptions. Typical methods in this branch map an input query (textual descriptions associated with an entity and a relation) and its candidate entities into feature vectors, respectively, and then maximize the probability of valid triples. These methods are gaining promising performance and increasing attention for the rapid development of large language models. According to the property of the language models, the more related and specific context information the input query provides, the more discriminative the resultant embedding will be. In this paper, through observation and validation, we find a neglected fact that the relation-aware neighbors of the head entities in queries could act as effective contexts for more precise link prediction. Driven by this finding, we propose a relation-aware anchor enhanced knowledge graph completion method (RAA-KGC). Specifically, in our method, to provide a reference of what might the target entity be like, we first generate anchor entities within the relation-aware neighborhood of the head entity. Then, by pulling the query embedding towards the neighborhoods of the anchors, it is tuned to be more discriminative for target entity matching. The results of our extensive experiments not only validate the efficacy of RAA-KGC but also reveal that by integrating our relation-aware anchor enhancement strategy, the performance of current leading methods can be notably enhanced without substantial modifications.

EvMic: Event-based Non-contact sound recovery from effective spatial-temporal modeling

When sound waves hit an object, they induce vibrations that produce high-frequency and subtle visual changes, which can be used for recovering the sound. Early studies always encounter trade-offs related to sampling rate, bandwidth, field of view, and the simplicity of the optical path. Recent advances in event camera hardware show good potential for its application in visual sound recovery, because of its superior ability in capturing high-frequency signals. However, existing event-based vibration recovery methods are still sub-optimal for sound recovery. In this work, we propose a novel pipeline for non-contact sound recovery, fully utilizing spatial-temporal information from the event stream. We first generate a large training set using a novel simulation pipeline. Then we designed a network that leverages the sparsity of events to capture spatial information and uses Mamba to model long-term temporal information. Lastly, we train a spatial aggregation block to aggregate information from different locations to further improve signal quality. To capture event signals caused by sound waves, we also designed an imaging system using a laser matrix to enhance the gradient and collected multiple data sequences for testing. Experimental results on synthetic and real-world data demonstrate the effectiveness of our method.

1-Tb/s/λ Transmission over Record 10714-km AR-HCF

We present the first single-channel 1.001-Tb/s DP-36QAM-PCS recirculating transmission over 73 loops of 146.77-km ultra-low-loss & low-IMI DNANF-5 fiber, achieving a record transmission distance of 10,714.28 km.

Chain of Correction for Full-text Speech Recognition with Large Language Models

Full-text error correction with Large Language Models (LLMs) for Automatic Speech Recognition (ASR) has gained increased attention due to its potential to correct errors across long contexts and address a broader spectrum of error types, including punctuation restoration and inverse text normalization. Nevertheless, many challenges persist, including issues related to stability, controllability, completeness, and fluency. To mitigate these challenges, this paper proposes the Chain of Correction (CoC) for full-text error correction with LLMs, which corrects errors segment by segment using pre-recognized text as guidance within a regular multi-turn chat format. The CoC also uses pre-recognized full text for context, allowing the model to better grasp global semantics and maintain a comprehensive overview of the entire content. Utilizing the open-sourced full-text error correction dataset ChFT, we fine-tune a pre-trained LLM to evaluate the performance of the CoC framework. Experimental results demonstrate that the CoC effectively corrects errors in full-text ASR outputs, significantly outperforming baseline and benchmark systems. We further analyze how to set the correction threshold to balance under-correction and over-rephrasing, extrapolate the CoC model on extremely long ASR outputs, and investigate whether other types of information can be employed to guide the error correction process.

Think Small, Act Big: Primitive Prompt Learning for Lifelong Robot Manipulation

Building a lifelong robot that can effectively leverage prior knowledge for continuous skill acquisition remains significantly challenging. Despite the success of experience replay and parameter-efficient methods in alleviating catastrophic forgetting problem, naively applying these methods causes a failure to leverage the shared primitives between skills. To tackle these issues, we propose Primitive Prompt Learning (PPL), to achieve lifelong robot manipulation via reusable and extensible primitives. Within our two stage learning scheme, we first learn a set of primitive prompts to represent shared primitives through multi-skills pre-training stage, where motion-aware prompts are learned to capture semantic and motion shared primitives across different skills. Secondly, when acquiring new skills in lifelong span, new prompts are appended and optimized with frozen pretrained prompts, boosting the learning via knowledge transfer from old skills to new ones. For evaluation, we construct a large-scale skill dataset and conduct extensive experiments in both simulation and real-world tasks, demonstrating PPL's superior performance over state-of-the-art methods.

Near-Field THz Bending Beamforming: A Convex Optimization Perspective

Terahertz (THz) communication systems suffer severe blockage issues, which may significantly degrade the communica tion coverage and quality. Bending beams, capable of adjusting their propagation direction to bypass obstacles, have recently emerged as a promising solution to resolve this issue by engineer ing the propagation trajectory of the beam. However, traditional bending beam generation methods rely heavily on the specific geometric properties of the propagation trajectory and can only achieve sub-optimal performance. In this paper, we propose a new and general bending beamforming method by adopting the convex optimization techniques. In particular, we formulate the bending beamforming design as a max-min optimization problem, aiming to optimize the analog or digital transmit beamforming vector to maximize the minimum received signal power among all positions along the bending beam trajectory. However, the resulting problem is non-convex and difficult to be solved optimally. To tackle this difficulty, we apply the successive convex approximation (SCA) technique to obtain a high-quality suboptimal solution. Numerical results show that our proposed bending beamforming method outperforms the traditional method and shows robustness to the obstacle in the environment.