Cellular networks have grown in size and complexity in recent years. To meet increasing traffic demands, new approaches are needed to replace legacy rule-based controllers and network management systems. Among these, learning-based methods are appealing because they can discover control policies without relying on expert knowledge. Intent-based networking, which describes desired network behavior rather than specific configurations, introduces a new level of abstraction. However, satisfying network intents under temporal constraints remains an open challenge. In this paper, we present a reinforcement learning approach that leverages Signal Temporal Logic (STL) to quantitatively translate network intents into a reward signal. We combine this with a transformer-based neural network architecture to handle temporal dependencies and multi-agent coordination. We evaluate our method in a high-fidelity telecommunications simulator, demonstrating that it outperforms state-of-the-art baselines. Our experiments show an improvement in satisfying temporally dependent intents compared to prior methods.

Informative path planning (IPP) applied to bathy- metric mapping allows AUVs to focus on feature-rich areas to quickly reduce uncertainty and increase mapping efficiency. Existing methods based on Bayesian optimization (BO) over Gaussian Process (GP) maps work well on small scenarios but they are short-sighted and computationally heavy when mapping larger areas, hindering deployment in real appli- cations. To overcome this, we present a 2-layered BO IPP method that performs non-myopic, real-time planning in a tree Search fashion over large Stochastic Variational GP maps, while respecting the AUV motion constraints and accounting for localization uncertainty. Our framework outperforms the standard industrial lawn-mowing pattern and a myopic baseline in a set of hardware in the loop (HIL) experiments in an embedded platform over real bathymetry. 

Useful robot control algorithms should not only achieve performance objectives but also adhere to hard safety constraints. Control Barrier Functions (CBFs) have been developed to provably ensure system safety through forward invariance. However, they often unnecessarily sacrifice performance for safety since they are purely reactive. Receding horizon control (RHC), on the other hand, consider planned trajectories to account for the future evolution of a system. This work provides a new perspective on safety-critical control by introducing Forward Invariance in Trajectory Spaces (FITS). We lift the problem of safe RHC into the trajectory space and describe the evolution of planned trajectories as a controlled dynamical system. Safety constraints defined over states can be converted into sets in the trajectory space which we render forward invariant via a CBF framework. We derive an efficient quadratic program (QP) to synthesize trajectories that provably satisfy safety constraints. Our experiments support that FITS improves the adherence to safety specifications without sacrificing performance over alternative CBF and NMPC methods.

The paper considers the route planning problem for a vehicle with limited capacity operating in a road network. The vehicle is assigned a set of transportation requests that are more complex than traveling between two locations, may involve dependencies between their sub-tasks, and include deadlines and priorities. The requests arrive gradually over the deployment time-horizon, and thus replanning is needed for new requests. We address cases when not all requests can be serviced by their deadlines despite car sharing. We introduce multiple quality measures for plans that account for requests' delays with respect to deadlines and priorities. We formalize the problem as planning in a weighted transition system under syntactically co-safe LTL formulas. We develop an online planning and replanning algorithm based on the automata-based approach to least-violating plan synthesis and on translation to a Mixed Integer Linear Program (MILP). Furthermore, we show that the MILP reduces to graph search for a subclass of quality measures that satisfy a monotonicity property. We show the approach in simulations, including a case study on the mid-Manhattan road network over the span of 24 hours.

Real-world human-robot interactions often encompass uncertainty. This uncertainty can be handled in different ways, for example by designing robot planners to be more or less risk-tolerant. However, how users actually perceive different risk-taking behaviours in robots has yet to be described. Additionally, in the absence of guarantees on optimal robot performance, the interaction between risk and performance on user perceptions is also unclear. To address this gap, we conducted a user study with 84 participants investigating how robot performance and risk behaviour affects users' trust and risk-taking decisions. Participants collaborated with a Franka robot arm to perform a block-stacking task. We compared a robot which displays consistent but sub-optimal behaviours to a robot displaying risky but occasionally optimal behaviour. Risky robot behaviour led to higher trust than consistent behaviour when the robot was on average good at stacking blocks (high expectation), but lower trust when the robot was on average bad at stacking blocks (low expectation). Individual risk-willingness also predicted likelihood of selecting the risky robot over the consistent robot for future interactions, but only when the average expectation was low. These findings have implications for risk-aware planning and decision-making in mixed human-robot systems.

Autonomous agents rely on sensor data to construct representations of their environments, essential for predicting future events and planning their actions. However, sensor measurements suffer from limited range, occlusions, and sensor noise. These challenges become more evident in highly dynamic environments. This work proposes a probabilistic framework to jointly infer which parts of an environment are statically and which parts are dynamically occupied. We formulate the problem as a Bayesian network and introduce minimal assumptions that significantly reduce the complexity of the problem. Based on those, we derive Transitional Grid Maps (TGMs), an efficient analytical solution. Using real data, we demonstrate how this approach produces better maps than the state-of-the-art by keeping track of both static and dynamic elements and, as a side effect, can help improve existing SLAM algorithms.

Reachable set computation is an important tool for analyzing control systems. Simulating a control system can show general trends, but a formal tool like reachability analysis can provide guarantees of correctness. Reachability analysis for complex control systems, e.g., with nonlinear dynamics and/or a neural network controller, is often either slow or overly conservative. To address these challenges, much literature has focused on spatial refinement, i.e., tuning the discretization of the input sets and intermediate reachable sets. This paper introduces the idea of temporal refinement: automatically choosing when along the horizon of the reachability problem to execute slow symbolic queries which incur less approximation error versus fast concrete queries which incur more approximation error. Temporal refinement can be combined with other refinement approaches as an additional tool to trade off tractability and tightness in approximate reachable set computation. We introduce a temporal refinement algorithm and demonstrate its effectiveness at computing approximate reachable sets for nonlinear systems with neural network controllers. We calculate reachable sets with varying computational budget and show that our algorithm can generate approximate reachable sets with a similar amount of error to the baseline in 20-70% less time.

Reachable set computation is an important tool for analyzing control systems. Simulating a control system can show general trends, but a formal tool like reachability analysis can provide guarantees of correctness. Reachability analysis for complex control systems, e.g., with nonlinear dynamics and/or a neural network controller, is often either slow or overly conservative. To address these challenges, much literature has focused on spatial refinement, i.e., tuning the discretization of the input sets and intermediate reachable sets. This paper introduces the idea of temporal refinement: automatically choosing when along the horizon of the reachability problem to execute slow symbolic queries which incur less approximation error versus fast concrete queries which incur more approximation error. Temporal refinement can be combined with other refinement approaches as an additional tool to trade off tractability and tightness in approximate reachable set computation. We introduce a temporal refinement algorithm and demonstrate its effectiveness at computing approximate reachable sets for nonlinear systems with neural network controllers. We calculate reachable sets with varying computational budget and show that our algorithm can generate approximate reachable sets with a similar amount of error to the baseline in 20-70% less time.

Limited visibility and sensor occlusions pose pressing safety challenges for advanced driver-assistance systems (ADAS) and autonomous vehicles (AVs). In this work, our pursuit was to strike a balance: a method that ensures safety in occluded scenarios while preventing overly cautious behavior. We argue that such approaches are crucial for AVs' future, particularly when navigating alongside human drivers on highways at high speeds. To this end, we used reachability analysis to find safe velocity bounds on occluded traffic participants. Compared to state-of-the-art methods, we achieved velocity increases in more than 60% of the 230 cut-in scenarios from the highD dataset, without sacrificing safety.