Novelty-based Sample Reuse for Continuous Robotics Control

In reinforcement learning, agents collect state information and rewards through environmental interactions, essential for policy refinement. This process is notably time-consuming, especially in complex robotic simulations and real-world applications. Traditional algorithms usually re-engage with the environment after processing a single batch of samples, thereby failing to fully capitalize on historical data. However, frequently observed states, with reliable value estimates, require minimal updates; in contrast, rare observed states necessitate more intensive updates for achieving accurate value estimations. To address uneven sample utilization, we propose Novelty-guided Sample Reuse (NSR). NSR provides extra updates for infrequent, novel states and skips additional updates for frequent states, maximizing sample use before interacting with the environment again. Our experiments show that NSR improves the convergence rate and success rate of algorithms without significantly increasing time consumption. Our code is publicly available at https://github.com/ppksigs/NSR-DDPG-HER.

GLRT-Based Metric Learning for Remote Sensing Object Retrieval

With the improvement in the quantity and quality of remote sensing images, content-based remote sensing object retrieval (CBRSOR) has become an increasingly important topic. However, existing CBRSOR methods neglect the utilization of global statistical information during both training and test stages, which leads to the overfitting of neural networks to simple sample pairs of samples during training and suboptimal metric performance. Inspired by the Neyman-Pearson theorem, we propose a generalized likelihood ratio test-based metric learning (GLRTML) approach, which can estimate the relative difficulty of sample pairs by incorporating global data distribution information during training and test phases. This guides the network to focus more on difficult samples during the training process, thereby encourages the network to learn more discriminative feature embeddings. In addition, GLRT is a more effective than traditional metric space due to the utilization of global data distribution information. Accurately estimating the distribution of embeddings is critical for GLRTML. However, in real-world applications, there is often a distribution shift between the training and target domains, which diminishes the effectiveness of directly using the distribution estimated on training data. To address this issue, we propose the clustering pseudo-labels-based fast parameter adaptation (CPLFPA) method. CPLFPA efficiently estimates the distribution of embeddings in the target domain by clustering target domain instances and re-estimating the distribution parameters for GLRTML. We reorganize datasets for CBRSOR tasks based on fine-grained ship remote sensing image slices (FGSRSI-23) and military aircraft recognition (MAR20) datasets. Extensive experiments on these datasets demonstrate the effectiveness of our proposed GLRTML and CPLFPA.

GSON: A Group-based Social Navigation Framework with Large Multimodal Model

As the number of service robots and autonomous vehicles in human-centered environments grows, their requirements go beyond simply navigating to a destination. They must also take into account dynamic social contexts and ensure respect and comfort for others in shared spaces, which poses significant challenges for perception and planning. In this paper, we present a group-based social navigation framework GSON to enable mobile robots to perceive and exploit the social group of their surroundings by leveling the visual reasoning capability of the Large Multimodal Model (LMM). For perception, we apply visual prompting techniques to zero-shot extract the social relationship among pedestrians and combine the result with a robust pedestrian detection and tracking pipeline to alleviate the problem of low inference speed of the LMM. Given the perception result, the planning system is designed to avoid disrupting the current social structure. We adopt a social structure-based mid-level planner as a bridge between global path planning and local motion planning to preserve the global context and reactive response. The proposed method is validated on real-world mobile robot navigation tasks involving complex social structure understanding and reasoning. Experimental results demonstrate the effectiveness of the system in these scenarios compared with several baselines.

Generalizing Alignment Paradigm of Text-to-Image Generation with Preferences through $f$-divergence Minimization

Direct Preference Optimization (DPO) has recently expanded its successful application from aligning large language models (LLMs) to aligning text-to-image models with human preferences, which has generated considerable interest within the community. However, we have observed that these approaches rely solely on minimizing the reverse Kullback-Leibler divergence during alignment process between the fine-tuned model and the reference model, neglecting the incorporation of other divergence constraints. In this study, we focus on extending reverse Kullback-Leibler divergence in the alignment paradigm of text-to-image models to $f$-divergence, which aims to garner better alignment performance as well as good generation diversity. We provide the generalized formula of the alignment paradigm under the $f$-divergence condition and thoroughly analyze the impact of different divergence constraints on alignment process from the perspective of gradient fields. We conduct comprehensive evaluation on image-text alignment performance, human value alignment performance and generation diversity performance under different divergence constraints, and the results indicate that alignment based on Jensen-Shannon divergence achieves the best trade-off among them. The option of divergence employed for aligning text-to-image models significantly impacts the trade-off between alignment performance (especially human value alignment) and generation diversity, which highlights the necessity of selecting an appropriate divergence for practical applications.

Range-SLAM: Ultra-Wideband-Based Smoke-Resistant Real-Time Localization and Mapping

This paper presents Range-SLAM, a real-time, lightweight SLAM system designed to address the challenges of localization and mapping in environments with smoke and other harsh conditions using Ultra-Wideband (UWB) signals. While optical sensors like LiDAR and cameras struggle in low-visibility environments, UWB signals provide a robust alternative for real-time positioning. The proposed system uses general UWB devices to achieve accurate mapping and localization without relying on expensive LiDAR or other dedicated hardware. By utilizing only the distance and Received Signal Strength Indicator (RSSI) provided by UWB sensors in relation to anchors, we combine the motion of the tag-carrying agent with raycasting algorithm to construct a 2D occupancy grid map in real time. To enhance localization in challenging conditions, a Weighted Least Squares (WLS) method is employed. Extensive real-world experiments, including smoke-filled environments and simulated

Probing the Safety Response Boundary of Large Language Models via Unsafe Decoding Path Generation

Large Language Models (LLMs) are implicit troublemakers. While they provide valuable insights and assist in problem-solving, they can also potentially serve as a resource for malicious activities. Implementing safety alignment could mitigate the risk of LLMs generating harmful responses. We argue that: even when an LLM appears to successfully block harmful queries, there may still be hidden vulnerabilities that could act as ticking time bombs. To identify these underlying weaknesses, we propose to use a cost value model as both a detector and an attacker. Trained on external or self-generated harmful datasets, the cost value model could successfully influence the original safe LLM to output toxic content in decoding process. For instance, LLaMA-2-chat 7B outputs 39.18% concrete toxic content, along with only 22.16% refusals without any harmful suffixes. These potential weaknesses can then be exploited via prompt optimization such as soft prompts on images. We name this decoding strategy: Jailbreak Value Decoding (JVD), emphasizing that seemingly secure LLMs may not be as safe as we initially believe. They could be used to gather harmful data or launch covert attacks.

ViIK: Flow-based Vision Inverse Kinematics Solver with Fusing Collision Checking

Inverse Kinematics (IK) is to find the robot's configurations that satisfy the target pose of the end effector. In motion planning, diverse configurations were required in case a feasible trajectory was not found. Meanwhile, collision checking (CC), e.g. Oriented bounding box (OBB), Discrete Oriented Polytope (DOP), and Quickhull \cite{quickhull}, needs to be done for each configuration provided by the IK solver to ensure every goal configuration for motion planning is available. This means the classical IK solver and CC algorithm should be executed repeatedly for every configuration. Thus, the preparation time is long when the required number of goal configurations is large, e.g. motion planning in cluster environments. Moreover, structured maps, which might be difficult to obtain, were required by classical collision-checking algorithms. To sidestep such two issues, we propose a flow-based vision method that can output diverse available configurations by fusing inverse kinematics and collision checking, named Vision Inverse Kinematics solver (ViIK). Moreover, ViIK uses RGB images as the perception of environments. ViIK can output 1000 configurations within 40 ms, and the accuracy is about 3 millimeters and 1.5 degrees. The higher accuracy can be obtained by being refined by the classical IK solver within a few iterations. The self-collision rates can be lower than 2%. The collision-with-env rates can be lower than 10% in most scenes. The code is available at: https://github.com/AdamQLMeng/ViIK.

QPO: Query-dependent Prompt Optimization via Multi-Loop Offline Reinforcement Learning

Prompt engineering has demonstrated remarkable success in enhancing the performance of large language models (LLMs) across diverse tasks. However, most existing prompt optimization methods only focus on the task-level performance, overlooking the importance of query-preferred prompts, which leads to suboptimal performances. Additionally, these methods rely heavily on frequent interactions with LLMs to obtain feedback for guiding the optimization process, incurring substantial redundant interaction costs. In this paper, we introduce Query-dependent Prompt Optimization (QPO), which leverages multi-loop offline reinforcement learning to iteratively fine-tune a small pretrained language model to generate optimal prompts tailored to the input queries, thus significantly improving the prompting effect on the large target LLM. We derive insights from offline prompting demonstration data, which already exists in large quantities as a by-product of benchmarking diverse prompts on open-sourced tasks, thereby circumventing the expenses of online interactions. Furthermore, we continuously augment the offline dataset with the generated prompts in each loop, as the prompts from the fine-tuned model are supposed to outperform the source prompts in the original dataset. These iterative loops bootstrap the model towards generating optimal prompts. Experiments on various LLM scales and diverse NLP and math tasks demonstrate the efficacy and cost-efficiency of our method in both zero-shot and few-shot scenarios.

Trajectory Planning for Teleoperated Space Manipulators Using Deep Reinforcement Learning

Trajectory planning for teleoperated space manipulators involves challenges such as accurately modeling system dynamics, particularly in free-floating modes with non-holonomic constraints, and managing time delays that increase model uncertainty and affect control precision. Traditional teleoperation methods rely on precise dynamic models requiring complex parameter identification and calibration, while data-driven methods do not require prior knowledge but struggle with time delays. A novel framework utilizing deep reinforcement learning (DRL) is introduced to address these challenges. The framework incorporates three methods: Mapping, Prediction, and State Augmentation, to handle delays when delayed state information is received at the master end. The Soft Actor Critic (SAC) algorithm processes the state information to compute the next action, which is then sent to the remote manipulator for environmental interaction. Four environments are constructed using the MuJoCo simulation platform to account for variations in base and target fixation: fixed base and target, fixed base with rotated target, free-floating base with fixed target, and free-floating base with rotated target. Extensive experiments with both constant and random delays are conducted to evaluate the proposed methods. Results demonstrate that all three methods effectively address trajectory planning challenges, with State Augmentation showing superior efficiency and robustness.

LVCP: LiDAR-Vision Tightly Coupled Collaborative Real-time Relative Positioning

In air-ground collaboration scenarios without GPS and prior maps, the relative positioning of drones and unmanned ground vehicles (UGVs) has always been a challenge. For a drone equipped with monocular camera and an UGV equipped with LiDAR as an external sensor, we propose a robust and real-time relative pose estimation method (LVCP) based on the tight coupling of vision and LiDAR point cloud information, which does not require prior information such as maps or precise initial poses. Given that large-scale point clouds generated by 3D sensors has more accurate spatial geometric information than the feature point cloud generated by image, we utilize LiDAR point clouds to correct the drift in visual-inertial odometry (VIO) when the camera undergoes significant shaking or the IMU has a low signal-to-noise ratio. To achieve this, we propose a novel coarse-to-fine framework for LiDAR-vision collaborative localization. In this framework, we construct point-plane association based on spatial geometric information, and innovatively construct a point-aided Bundle Adjustment (BA) problem as the backend to simultaneously estimate the relative pose of the camera and LiDAR and correct the VIO drift. In this process, we propose a particle swarm optimization (PSO) based sampling algorithm to complete the coarse estimation of the current camera-LiDAR pose. In this process, the initial pose of the camera used for sampling is obtained based on VIO propagation, and the valid feature-plane association number (VFPN) is used to trigger PSO-sampling process. Additionally, we propose a method that combines Structure from Motion (SFM) and multi-level sampling to initialize the algorithm, addressing the challenge of lacking initial values.