Safe navigation is essential for autonomous systems operating in hazardous environments. Traditional planning methods are effective for solving long-horizon tasks but depend on the availability of a graph representation with prede-fined distance metrics. In contrast, safe Reinforcement Learning (RL) is capable of learning complex behaviors without relying on manual heuristics but fails to solve long-horizon tasks, particularly in goal-conditioned and multi-agent scenarios. In this paper, we introduce a novel method that integrates the strengths of both planning and safe RL. Our method leverages goal-conditioned RL (GCRL) and safe RL to learn a goal-conditioned policy for navigation while concurrently estimating cumulative distance and safety levels using learned value functions via an automated self-training algorithm. By constructing a graph with states from the replay buffer, our method prunes unsafe edges and generates a waypoint-based plan that the agent then executes by following those waypoints sequentially until their goal locations are reached. This graph pruning and planning approach via the learned value functions allows our approach to flexibly balance the trade-off between faster and safer routes especially over extended horizons. Utilizing this unified high-level graph and a shared low-level safe GCRL policy, we extend this approach to address the multi-agent safe navigation problem. In particular, we leverage Conflict-Based Search (CBS) to create waypoint-based plans for multiple agents allowing for their safer navigation over extended horizons. This integration enhances the scalability of goal-conditioned safe RL in multi-agent scenarios, enabling efficient coordination among agents. Extensive benchmarking against state-of-the-art baselines demonstrates the effectiveness of our method in achieving distance goals safely for multiple agents in complex and hazardous environments. Our code and further details about or work is available at https://safe-visual-mapf-mers.csail.mit.edu/.

Vehicular Ad-hoc Networks (VANETs) in intelligent transportation systems (ITS) facilitate communication and data exchange between vehicles and roadside infrastructure. Although integrating blockchain technology with VANETs enhances security and reliability, it poses significant challenges due to the high computational demands of the consensus process, leading to resource consumption and data processing delays. To address these issues, we introduce software-defined road-side units (SD-RSUs) that leverage Software-Defined Everything principles to transform traditional RSUs into multifunction, adaptive edge nodes, thereby improving network efficiency. We propose a novel network architecture for blockchain-integrated VANETs featuring Comprehensive Validator Nodes (CVNs) and Streamlined Interact Nodes (SINs). Simulations demonstrate that our approach enhances edge computing tasks and scalability while mitigating service disruptions typically associated with blockchain computations.

Performance and robustness of real-world Acoustic Event Classification (AEC) solutions depend on ability to train on diverse data from wide range of end-point devices and acoustic environments. Federated Learning (FL) provides a framework to leverage annotated and non-annotated AEC data from servers and client devices in a privacy preserving manner. In this work we propose a novel Federated Relaxed Pareto Optimization (FedRPO) method for semi-supervised FL with heterogeneous client data. In contrast to federated averaging class of FL algorithms (fedAvg) that perform unconstrained weighted aggregation across all data sources, FedRPO enables special treatment of data with high quality annotations vs. data with pseudo-labels of unknown, varying qualities. In particular, FedRPO computes the updates to the global model solving a constrained linear program, with explicit Pareto constraints to prevent performance degradation on annotated data, and controlled relaxation of the Pareto constraints on pseudo-labeled data to prevent learning of patterns in conflict with the annotated data. We show FedRPO significantly outperforms FedAvg on Amazon internal de-identified dataset on AEC tasks. On supervised learning, FedRPO improved precision by 32.5% over FedAvg when maintaining recall at 90%. Combined with FixMatch [1] for semi-supervised learning, FedRPO outperformed FedAvg on precision by 50.5% at 90% recall.

Standard acoustic event classification (AEC) solutions require large-scale collection of data from client devices for model optimization. Federated learning (FL) is a compelling frame- work that decouples data collection and model training to enhance customer privacy. In this work, we investigate the feasibility of applying FL to improve AEC performance while no customer data can be directly uploaded to the server. We assume no pseudo labels can be inferred from on-device user inputs, aligning with the typical use cases of AEC. We adapt self-supervised learning to the FL framework for on-device continual learning of representations, and it results in improved performance of the downstream AEC classifiers with- out labeled/pseudo-labeled data available. Compared to the baseline w/o FL, the proposed method improves precision up to 20.3% relatively while maintaining the recall. Our work differs from prior work in FL in that our approach does not require user-generated learning targets, and the data we use is collected from our Beta program and is de-identified, to maximally simulate the production settings.

Risk-bounded motion planning is an important yet difficult problem for safety-critical tasks. While existing mathematical programming methods offer theoretical guarantees in the context of constrained Markov decision processes, they either lack scalability in solving larger problems or produce conservative plans. Recent advances in deep reinforcement learning improve scalability by learning policy networks as function approximators. In this paper, we propose an extension of soft actor critic model to estimate the execution risk of a plan through a risk critic and produce risk-bounded policies efficiently by adding an extra risk term in the loss function of the policy network. We define the execution risk in an accurate form, as opposed to approximating it through a summation of immediate risks at each time step that leads to conservative plans. Our proposed model is conditioned on a continuous spectrum of risk bounds, allowing the user to adjust the risk-averse level of the agent on the fly. Through a set of experiments, we show the advantage of our model in terms of both computational time and plan quality, compared to a state-of-the-art mathematical programming baseline, and validate its performance in more complicated scenarios, including nonlinear dynamics and larger state space.