In Human–Robot Interaction (HRI), vandalization of robots is predominantly framed as senseless or immoral behaviors, with mitigation efforts focused on system design improvements or user education. Drawing on sociological theories of vandalism, we examine three cases of robot destruction in public spaces: the burning of Waymo vehicles in Los Angeles (2025), the vandalism of a Knightscope K5 robot in San Francisco (2017), and the destruction of the hitchhiking robot hitchBOT in Philadelphia (2015). We argue that not all acts of robot vandalism are instances of "malicious" destruction. Rather, some can be understood as ideological or political vandalism - expressive or strategic acts embedded within broader social and political struggles. By situating these events in their urban and discursive contexts, our analysis moves beyond explanations grounded solely in individual psychology or system design, and invites a broader reflection within HRI on how robots as socio-technical artifacts become implicated in the politics of public space, power, and collective life.

In this paper, we address multi-robot formation planning where nonholonomic robots collaboratively transport objects using a deformable sheet in unstructured, cluttered environments. The formation can expand or contract to adjust the height of the object on the sheet. However, interactions between the robots and sheet introduce complex constraints for formation planning. Complexity increases further when the only feasible solution requires crossing an obstacle, i.e. where robots navigate in different homotopy classes around an obstacle such that the object hovers above it. Most existing nonholonomic formation planners do not admit obstacle crossing, limiting performance. In this paper, we present a two-stage iterative trajectory optimization framework which explicitly considers obstacle crossing. First, we capture the set of all feasible homotopy classes for each robot using a topological probabilistic roadmap. We then iteratively apply numerical optimization techniques to find a safe and feasible solution for the formation. We demonstrate the efficacy of our framework in simulation and on real robot hardware.

Behaviour trees (BTs) are popular within robotics due to their reactivity, reusability, and modularity. BTs are often designed by hand using expert domain knowledge. However, robot environments contain sources of uncertainty which affect robot behaviour. It is challenging for human designers to reason over the effects of uncertainty up to the task horizon, limiting robot performance. For example, the chance of an unexpected blockage late along a robot’s route should encourage the robot to take an alternate path. Therefore, in this paper we refine the task-level behaviour encoded in a BT through planning under uncertainty. The refinement process modifies when action nodes are executed by reasoning over the effects of uncertainty, improving task performance. We first extract a state space from the BT and learn a set of Bayesian networks (BNs) which model the stochastic dynamics of robot actions. We then use the extracted state space and BNs to construct and solve a Markov decision process which captures robot execution. This produces a policy which describes the refined behaviour. We empirically demonstrate how our approach reduces the completion time for robot navigation and search tasks.

A common task in cinematography is tracking a subject or character through a scene. For complex setups, multiple cameras must track the subject simultaneously to attain sufficient coverage. Recently, researchers have considered using multiple camera-mounted autonomous mobile robots for this task. Existing work is limited to UAVs, which may be unavailable due to cost, safety requirements, or flight restrictions. Therefore, in this paper we present a tracking approach for complex and unstructured environments using differential-drive robots with gimbal-mounted cameras. Differential-Drive robots pose a challenge, as their movement is more restricted than UAVs. For this, we introduce a novel hierarchical planning framework which ensures safety and visibility while maximizing shot diversity. We begin by synthesizing a set of paths using sequential greedy viewpoint planning and conflict-based search under a set of optimal viewpoint constraints. These paths then form an initial guess for joint trajectory optimization, which synthesizes stable trajectories under the motion constraints of the robots and gimbals. Empirically, we show how our approach outperforms approaches aimed at UAVs, which may synthesize infeasible trajectories when applied to differential-drive robots.

To efficiently cover an environment, robots must be able to handle changes in occupancy during execution. These occupancy dynamics are often stochastic, and so we cannot deterministically predict when a location will be occupied. Existing coverage solutions either assume static environments or react to changes as they occur, limiting performance. In this paper we present a framework for lifelong area coverage under spatiotemporal uncertainty, where a robot repeats coverage over multiple episodes. The stochastic occupancy dynamics are a priori unknown and learned using the observations received during coverage. For each coverage episode, we build and solve a partially observable Markov decision process which exploits our learned spatiotemporal dynamics model to improve performance. We demonstrate the efficacy of our framework across extensive experiments in synthetic environments.

This paper deals with Multi-robot Trajectory Planning, that is, the problem of computing trajectories for multiple robots navigating in a shared space while minimizing for control energy. Approaches based on trajectory optimization can solve this problem optimally. However, such methods are hampered by complex robot dynamics and collision constraints that couple robot's decision variables. We propose a distributed multi-robot optimization algorithm (DiMOpt) that addresses these issues by exploiting (1) consensus optimization strategies to tackle coupling collision constraints, and (2) a single-robot sequential convex programming method for efficiently handling non-convexities introduced by dynamics. We compare DiMOpt with a baseline centralized multi-robot sequential convex programming algorithm (SCP). We empirically demonstrate that DiMOpt scales well for large fleets of robots while computing solutions faster and with lower costs. Finally, DiMOpt is an iterative algorithm that finds feasible trajectories before converging to a locally optimal solution, and results suggest the quality of such fast initial solutions is comparable to a converged solution computed via SCP.

This paper addresses the problem of automating fleets of robots with non-holonomic dynamics. Previously studied methods either specialize in facets of this problem, that is, one or a combination of multi-robot goal allocation, motion planning, and coordination, and typically acrifice optimality and completeness for scalability. We propose an approach that constructs an abstract multi-robot roadmap in a reduced configuration space, where we account for environment connectivity and interference cost between robots occupying the same polygons. Querying the road-map results in a robot-goal assignment and abstract multi-robot trajectory. This is then exploited to de-compose the original problem into smaller problems, each of which is solved with a multi-robot motion planner that accounts for kinodynamic constraints. We validate the approach experimentally to demonstrate the advantage of considering task assignment and motion planning holistically, and explore some methods for balancing solution quality and computational efficiency.

Modern lightweight dual-arm robots bring the physical capabilities to quickly take over tasks at typical industrial workplaces designed for workers. In times of mass-customization, low setup times including the instructing/specifying of new tasks are crucial to stay competitive. We propose a constraint programming approach to simultaneous task allocation and motion scheduling for such industrial manipulation and assembly tasks. The proposed approach covers dual-arm and even multi-arm robots as well as connected machines. The key concept are Ordered Visiting Constraints, a descriptive and extensible model to specify such tasks with their spatiotemporal requirements and task-specific combinatorial or ordering constraints. Our solver integrates such task models and robot motion models into constraint optimization problems and solves them efficiently using various heuristics to produce makespan-optimized robot programs. The proposed task model is robot independent and thus can easily be deployed to other robotic platforms. Flexibility and portability of our proposed model is validated through several experiments on different simulated robot platforms. We benchmarked our search strategy against a general-purpose heuristic. For large manipulation tasks with 200 objects, our solver implemented using Google's Operations Research tools and ROS requires less than a minute to compute usable plans.

We present a novel modelling and planning approach for multi-robot systems under uncertain travel times. The approach uses generalised stochastic Petri nets (GSPNs) to model desired team behaviour, and allows to specify safety constraints and rewards. The GSPN is interpreted as a Markov decision process (MDP) for which we can generate policies that optimise the requirements. This representation is more compact than the equivalent multi-agent MDP, allowing us to scale better. Furthermore, it naturally allows for asynchronous execution of the generated policies across the robots, yielding smoother team behaviour. We also describe how the integration of the GSPN with a lower-level team controller allows for accurate expectations on team performance. We evaluate our approach on an industrial scenario, showing that it outperforms hand-crafted policies used in current practice.

This paper is concerned with automating fleets of autonomous robots. This involves solving a multitude of problems, including goal assignment, motion planning, and coordination, while maximizing some performance criterion. While methods for solving these sub-problems have been studied, they address only a facet of the overall problem, and make strong assumptions on the use-case, on the environment, or on the robots in the fleet. In this paper, we formulate the overall fleet management problem in terms of Optimal Control. We describe a scheme for solving this problem in the particular case of fleets of non-holonomic robots navigating in an environment with obstacles. The method is based on a two-phase approach, whereby the first phase solves for fleet-wide boolean decision variables via Mixed Integer Quadratic Programming, and the second phase solves for real-valued variables to obtain an optimized set of trajectories for the fleet. Examples showcasing the features of the method are illustrated, and the method is validated experimentally.

We introduce a variant of the multi-vehicle routing problem which accounts for nonholonomic constraints and dense, dynamic obstacles, called MVRP-DDO. The problem is strongly motivated by an industrial mining application. This paper illustrates how MVRP-DDO relates to other extensions of the vehicle routing problem. We provide an application-independent formulation of MVRP-DDO, as well as a concrete instantiation in a surface mining application. We propose a multi-abstraction search approach to compute an executable plan for the drilling operations of several machines in a very constrained environment. The approach is evaluated in terms of makespan and computation time, both of which are hard industrial requirements.

An important contribution of AI to Robotics is the model-centred approach, whereby competent robot behaviour stems from automated reasoning in models of the world which can be changed to suit different environments, physical capabilities and tasks. However models need to capture diverse (and often application-dependent) aspects of the robot’s environment and capabilities. They must also have good computational properties, as robots need to reason while they act in response to perceived context. In this article, we investigate the use of a meta-CSP-based technique to interleave reasoning in diverse knowledge types. We reify the approach through a robotic waiter case study, for which a particular selection of spatial, temporal, resource and action KR formalisms is made. Using this case study, we discuss general principles pertaining to the selection of appropriate KR formalisms and jointly reasoning about them. The resulting integration is evaluated both formally and experimentally on real and simulated robotic platforms.

Plan-based robot control has to consider a multitude of aspects of tasks at once, such as task dependency, time, space, and resource usage. Hybrid planning is a strategy for treating them jointly. However, by incorporating all these aspects into a hybrid planner, its search space is huge by construction. This paper introduces the planner CHIMP, which is based on meta-CSP planning to represent the hybrid plan space and uses hierarchical planning as the strategy for cutting efficiently through this space. The paper makes two contributions: First, it describes how HTN planning is integrated into meta-CSP reasoning leading to a planner that can reason about different forms of knowledge and that is fast enough to be used on a robot. Second, it demonstrates CHIMP's task merging capabilities, i.e., the unification of different tasks from different plan parts, resulting in plans that are more efficient to execute. It also allows to merge new tasks online into a plan that is being executed. This is demonstrated on a PR2 robot.

AI-based solutions for robot planning have so far focused on very high-level abstractions of robot capabilities and of the environment in which they operate. However, to be useful in a robotic context, the model provided to an AI planner should afford both symbolic and metric constructs; its expressiveness should not hinder computational efficiency; and it should include causal, spatial, temporal and resource aspects of the domain. We propose a planner grounded on well-founded constraint-based calculi that adhere to these requirements. A proof of completeness is provided, and the flexibility and portability of the approach is validated through several experiments on real and simulated robot platforms.