Small Unmanned Aerial Systems (sUAS) must meet rigorous safety standards when deployed in high-stress emergency response scenarios. However, tests that execute perfectly in simulation can fail dramatically in real-world environments. Fuzz testing can be used to increase system robustness by providing malformed input data aimed at triggering failure cases. In this paper, we apply fuzzing to support human interaction testing. Initial tests are run in simulation to provide broad coverage of the input space in a safe environment; however, they lack the fidelity of real-world tests. Field tests provide higher fidelity but can result in costly or dangerous crashes. We, therefore, propose and demonstrate HiFuzz, which executes large numbers of fuzz tests in simulation and then down-selects tests for deployment in human-in-the-loop simulations and safety-aware physical field tests. We apply \hf to a multi-sUAS system and show that each test level serves a unique purpose in identifying known and unknown failures associated with human interactions.
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Neural radiance fields (NeRFs) have gained popularity in the autonomous driving (AD) community. Recent methods show NeRFs' potential for closed-loop simulation, enabling testing of AD systems, and as an advanced training data augmentation technique. However, existing methods often require long training times, dense semantic supervision, or lack generalizability. This, in turn, hinders the application of NeRFs for AD at scale. In this paper, we propose NeuRAD, a robust novel view synthesis method tailored to dynamic AD data. Our method features simple network design, extensive sensor modeling for both camera and lidar -- including rolling shutter, beam divergence and ray dropping -- and is applicable to multiple datasets out of the box. We verify its performance on five popular AD datasets, achieving state-of-the-art performance across the board. To encourage further development, we will openly release the NeuRAD source code. See //github.com/georghess/NeuRAD .
Existing Graph Neural Network (GNN) training frameworks have been designed to help developers easily create performant GNN implementations. However, most existing GNN frameworks assume that the input graphs are static, but ignore that most real-world graphs are constantly evolving. Though many dynamic GNN models have emerged to learn from evolving graphs, the training process of these dynamic GNNs is dramatically different from traditional GNNs in that it captures both the spatial and temporal dependencies of graph updates. This poses new challenges for designing dynamic GNN training frameworks. First, the traditional batched training method fails to capture real-time structural evolution information. Second, the time-dependent nature makes parallel training hard to design. Third, it lacks system supports for users to efficiently implement dynamic GNNs. In this paper, we present NeutronStream, a framework for training dynamic GNN models. NeutronStream abstracts the input dynamic graph into a chronologically updated stream of events and processes the stream with an optimized sliding window to incrementally capture the spatial-temporal dependencies of events. Furthermore, NeutronStream provides a parallel execution engine to tackle the sequential event processing challenge to achieve high performance. NeutronStream also integrates a built-in graph storage structure that supports dynamic updates and provides a set of easy-to-use APIs that allow users to express their dynamic GNNs. Our experimental results demonstrate that, compared to state-of-the-art dynamic GNN implementations, NeutronStream achieves speedups ranging from 1.48X to 5.87X and an average accuracy improvement of 3.97%.
The Open Radio Access Network (O-RAN) architecture empowers intelligent and automated optimization of the RAN through applications deployed on the RAN Intelligent Controller (RIC) platform, enabling capabilities beyond what is achievable with traditional RAN solutions. Within this paradigm, Traffic Steering (TS) emerges as a pivotal RIC application that focuses on optimizing cell-level mobility settings in near-real-time, aiming to significantly improve network spectral efficiency. In this paper, we design a novel TS algorithm based on a Cascade Reinforcement Learning (CaRL) framework. We propose state space factorization and policy decomposition to reduce the need for large models and well-labeled datasets. For each sub-state space, an RL sub-policy will be trained to learn an optimized mapping onto the action space. To apply CaRL on new network regions, we propose a knowledge transfer approach to initialize a new sub-policy based on knowledge learned by the trained policies. To evaluate CaRL, we build a data-driven and scalable RIC digital twin (DT) that is modeled using important real-world data, including network configuration, user geo-distribution, and traffic demand, among others, from a tier-1 mobile operator in the US. We evaluate CaRL on two DT scenarios representing two network clusters in two different cities and compare its performance with the business-as-usual (BAU) policy and other competing optimization approaches using heuristic and Q-table algorithms. Benchmarking results show that CaRL performs the best and improves the average cluster-aggregated downlink throughput over the BAU policy by 24% and 18% in these two scenarios, respectively.
Many autonomous systems face safety challenges, requiring robust closed-loop control to handle physical limitations and safety constraints. Real-world systems, like autonomous ships, encounter nonlinear dynamics and environmental disturbances. Reinforcement learning is increasingly used to adapt to complex scenarios, but standard frameworks ensuring safety and stability are lacking. Predictive Safety Filters (PSF) offer a promising solution, ensuring constraint satisfaction in learning-based control without explicit constraint handling. This modular approach allows using arbitrary control policies, with the safety filter optimizing proposed actions to meet physical and safety constraints. We apply this approach to marine navigation, combining RL with PSF on a simulated Cybership II model. The RL agent is trained on path following and collision avpodance, while the PSF monitors and modifies control actions for safety. Results demonstrate the PSF's effectiveness in maintaining safety without hindering the RL agent's learning rate and performance, evaluated against a standard RL agent without PSF.
In Vehicle-to-Everything (V2X) systems, reliable and secure information exchange plays a pivotal role in road safety and traffic management. Due to the open nature of the wireless medium and the constant or intermittent mobility of vehicles, the security of transmissions in V2X is more challenging compared to traditional wireless networks. Physical layer security (PLS) leverages the inherent randomness of wireless communication channels to ensure the confidentiality and security of information transmission. Current PLS schemes in integrated communications and sensing (ISAC) enabled V2X systems is to utilize communication interference to significantly impact the eavesdropping channel more than the legitimate channel. However, in an ISAC-enabled V2X system, it is crucial to prioritize and address the issue of interference coupling as it significantly impacts the confidentiality and security of information exchange. This goes beyond simply relying on the communication interference. Until now, no discussions or studies on integrating security with ISAC (Seucue-ISAC) in ISAC-enabled V2X systems, specifically regarding the exploitation of sensing interference or coupling interference. In this article, we provide a comprehensive review on PLS metrics and security threats encountered in V2X communication. And then, we discuss and analyze four popular PLS techniques and the main challenges associated with their implementation in ISAC-enabled V2X systems. Finally, we share our vision for PLS studies in ISAC-based V2X systems to promote Secure-ISAC.
We present RobotGPT, an innovative decision framework for robotic manipulation that prioritizes stability and safety. The execution code generated by ChatGPT cannot guarantee the stability and safety of the system. ChatGPT may provide different answers for the same task, leading to unpredictability. This instability prevents the direct integration of ChatGPT into the robot manipulation loop. Although setting the temperature to 0 can generate more consistent outputs, it may cause ChatGPT to lose diversity and creativity. Our objective is to leverage ChatGPT's problem-solving capabilities in robot manipulation and train a reliable agent. The framework includes an effective prompt structure and a robust learning model. Additionally, we introduce a metric for measuring task difficulty to evaluate ChatGPT's performance in robot manipulation. Furthermore, we evaluate RobotGPT in both simulation and real-world environments. Compared to directly using ChatGPT to generate code, our framework significantly improves task success rates, with an average increase from 38.5% to 91.5%. Therefore, training a RobotGPT by utilizing ChatGPT as an expert is a more stable approach compared to directly using ChatGPT as a task planner.
The quest for fully autonomous vehicles (AVs) capable of navigating complex real-world scenarios with human-like understanding and responsiveness. In this paper, we introduce Dolphins, a novel vision-language model architected to imbibe human-like abilities as a conversational driving assistant. Dolphins is adept at processing multimodal inputs comprising video (or image) data, text instructions, and historical control signals to generate informed outputs corresponding to the provided instructions. Building upon the open-sourced pretrained Vision-Language Model, OpenFlamingo, we first enhance Dolphins's reasoning capabilities through an innovative Grounded Chain of Thought (GCoT) process. Then we tailored Dolphins to the driving domain by constructing driving-specific instruction data and conducting instruction tuning. Through the utilization of the BDD-X dataset, we designed and consolidated four distinct AV tasks into Dolphins to foster a holistic understanding of intricate driving scenarios. As a result, the distinctive features of Dolphins are characterized into two dimensions: (1) the ability to provide a comprehensive understanding of complex and long-tailed open-world driving scenarios and solve a spectrum of AV tasks, and (2) the emergence of human-like capabilities including gradient-free instant adaptation via in-context learning and error recovery via reflection.
As safety is of paramount importance in robotics, reinforcement learning that reflects safety, called safe RL, has been studied extensively. In safe RL, we aim to find a policy which maximizes the desired return while satisfying the defined safety constraints. There are various types of constraints, among which constraints on conditional value at risk (CVaR) effectively lower the probability of failures caused by high costs since CVaR is a conditional expectation obtained above a certain percentile. In this paper, we propose a trust region-based safe RL method with CVaR constraints, called TRC. We first derive the upper bound on CVaR and then approximate the upper bound in a differentiable form in a trust region. Using this approximation, a subproblem to get policy gradients is formulated, and policies are trained by iteratively solving the subproblem. TRC is evaluated through safe navigation tasks in simulations with various robots and a sim-to-real environment with a Jackal robot from Clearpath. Compared to other safe RL methods, the performance is improved by 1.93 times while the constraints are satisfied in all experiments.
Testing autonomous vehicles (AVs) under various environmental scenarios that lead the vehicles to unsafe situations is known to be challenging. Given the infinite possible environmental scenarios, it is essential to find critical scenarios efficiently. To this end, we propose a novel testing method, named EpiTESTER, by taking inspiration from epigenetics, which enables species to adapt to sudden environmental changes. In particular, EpiTESTER adopts gene silencing as its epigenetic mechanism, which regulates gene expression to prevent the expression of a certain gene, and the probability of gene expression is dynamically computed as the environment changes. Given different data modalities (e.g., images, lidar point clouds) in the context of AV, EpiTESTER benefits from a multi-model fusion transformer to extract high-level feature representations from environmental factors and then calculates probabilities based on these features with the attention mechanism. To assess the cost-effectiveness of EpiTESTER, we compare it with a classical genetic algorithm (GA) (i.e., without any epigenetic mechanism implemented) and EpiTESTER with equal probability for each gene. We evaluate EpiTESTER with four initial environments from CARLA, an open-source simulator for autonomous driving research, and an end-to-end AV controller, Interfuser. Our results show that EpiTESTER achieved a promising performance in identifying critical scenarios compared to the baselines, showing that applying epigenetic mechanisms is a good option for solving practical problems.
We present Emu, a system that semantically enhances multilingual sentence embeddings. Our framework fine-tunes pre-trained multilingual sentence embeddings using two main components: a semantic classifier and a language discriminator. The semantic classifier improves the semantic similarity of related sentences, whereas the language discriminator enhances the multilinguality of the embeddings via multilingual adversarial training. Our experimental results based on several language pairs show that our specialized embeddings outperform the state-of-the-art multilingual sentence embedding model on the task of cross-lingual intent classification using only monolingual labeled data.
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