68 accepted papers
Multi-Robot Task Assignment (MRTA) studies the problem of allocating spatially distributed tasks to a fleet of cooperative robots as well as determining the optimal task sequence for each robot. Common objectives include minimizing the task waiting times, minimizing the robot tour lengths and maximi…
Autonomously performing tasks often requires robots to plan high-level discrete actions and continuous low-level motions to realize them. Previous TAMP algorithms have focused mainly on computational performance, completeness, or optimality by making the problem tractable through simplifications and
To efficiently deploy robotic systems in society, mobile robots need to autonomously and safely move through complex environments. Nonlinear model predictive control (MPC) methods provide a natural way to find a dynamically feasible trajectory through the environment without colliding with nearby ob…
Aerial manipulators, which combine robotic arms with multi-rotor drones, face strict constraints on arm weight and mechanical complexity. In this work, we study a lightweight 2-degree-of-freedom (DoF) arm mounted on a quadrotor via a differential mechanism, capable of full six-DoF end-effector pose …
We present a novel method for optimizing the posture of kinematically redundant torque-controlled robots to improve robustness during impacts. A rigid impact model is used as the basis for a configuration-dependent metric that quantifies the variation between pre- and post-impact velocities. By find…
The use of semantic features can improve the efficiency of target search in unknown environments for robotic search and rescue missions. Current target search methods rely on training with large datasets of similar domains, which limits the adaptability to diverse environments. However, human expert…
Deployment of robots in dynamic environments requires reactive trajectory generation. While optimization-based methods, such as Model Predictive Control focus on constraint verificaction, Geometric Fabrics offer a computationally efficient way to generate trajectories that include all avoidance beha…
Control Barrier Functions (CBFs) have proven to be an effective tool for performing safe control synthesis for nonlinear systems. However, guaranteeing safety in the presence of disturbances and input constraints for high relative degree systems is a difficult problem. In this work, we propose the R…
Diffusion policies have recently emerged as a powerful framework for robotic manipulation. However, like other behavior cloning methods, they remain vulnerable to distributional shift, often requiring human-in-the-loop interventions to correct failures during deployment. These interactions naturally…
Unmanned Surface Vessels (USVs) face significant control challenges due to uncertain environmental disturbances like waves and currents. This paper proposes a trajectory tracking controller based on Active Disturbance Rejection Control (ADRC) implemented on the DUS V2500. A custom simulation incorpo
This paper presents the first decentralized method to enable real-world 6-DoF manipulation of a cable-suspended load using a team of Micro-Aerial Vehicles (MAVs). Our method leverages multi-agent reinforcement learning (MARL) to train an outer-loop control policy for each MAV. Unlike state-of-the-ar…
Deploying mobile robots safely among humans requires the motion planner to account for the uncertainty in the other agents’ predicted trajectories. This remains challenging in traditional approaches, especially with arbitrarily shaped predictions and real-time constraints. To address these challenge
Mobile manipulators operating in dynamic environments shared with humans and robots must adapt in real time to environmental changes to complete their tasks effectively. While global planning methods are effective at considering the full task scope, they lack the computational efficiency required fo
Robot navigation methods allow mobile robots to operate in applications such as warehouses or hospitals. While the environment in which the robot operates imposes requirements on its navigation behavior, most existing methods do not allow the end-user to configure the robot's behavior and priorities
Deployment of robots in dynamic environments requires reactive trajectory generation. While optimization-based methods, such as Model Predictive Control focus on constraint verificaction, Geometric Fabrics offer a computationally efficient way to generate trajectories that include all avoidance beha
Navigation Among Movable Obstacles (NAMO) poses a challenge for traditional path-planning methods when obstacles block the path, requiring push actions to reach the goal. We propose a framework that enables movability-aware planning to overcome this challenge without relying on explicit obstacle pla
Control Barrier Functions (CBFs) have proven to be an effective tool for performing safe control synthesis for nonlinear systems. However, guaranteeing safety in the presence of disturbances and input constraints for high relative degree systems is a difficult problem. In this work, we propose the R
We present a sampling-based model predictive control method that uses a generic physics simulator as the dynamical model. In particular, we propose a Model Predictive Path Integral controller (MPPI) that employs the GPU-parallelizable IsaacGym simulator to compute the forward dynamics of the robot a
Motion planning for autonomous robots in dynamic environments poses numerous challenges due to uncertainties in the robot's dynamics and interaction with other agents. Sampling-based MPC approaches, such as Model Predictive Path Integral (MPPI) control, have shown promise in addressing these complex
Contingency planning, wherein an agent generates a set of possible plans conditioned on the outcome of an uncertain event, is an increasingly popular way for robots to act under uncertainty. In this work we take a game-theoretic perspective on contingency planning, tailored to multi-agent scenarios
As robots shift from industrial to human-centered spaces, adopting mobile manipulators, which expand workspace capabilities, becomes crucial. In these settings, seamless interaction with humans necessitates compliant control. Two common methods for safe interaction, admittance, and impedance control…
This paper proposes a decentralized trajectory planning framework for the collision avoidance problem of multiple micro aerial vehicles (MAVs) in environments with static and dynamic obstacles. The framework utilizes spatiotemporal occupancy grid maps (SOGM), which forecast the occupancy status of n…
Although autonomous robots have great potential to boost efficiency and throughput across the whole retail chain, they are mostly being deployed in large warehouses and distribution centers. Deploying robots in stores with customers, such as supermarkets, requires substantially more development effo…
Dynamic obstacle avoidance is a popular research topic for autonomous systems, such as micro aerial vehicles and service robots. Accurately evaluating the performance of dynamic obstacle avoidance methods necessitates the establishment of a metric to quantify the environment’s difficulty, a crucial
Task and Motion Planning (TAMP) has made strides in complex manipulation tasks, yet the execution robustness of the planned solutions remains overlooked. In this work, we propose a method for reactive TAMP to cope with runtime uncertainties and disturbances. We combine an Active Inference planner (A
Autonomous mobile robots require predictions of human motion to plan a safe trajectory that avoids them. Because human motion cannot be predicted exactly, future trajectories are typically inferred from real-world data via learning-based approximations. These approximations provide useful informatio…
The estimation of probability density functions is a fundamental problem in science and engineering. However, common methods such as kernel density estimation (KDE) have been demonstrated to lack robustness, while more complex methods have not been evaluated in multi-modal estimation problems. In th…
When designing a motion planner for autonomous robots there are usually multiple objectives to be considered. However, a cost function that yields the desired trade-off between objectives is not easily obtainable. A common technique across many applications is to use a weighted sum of relevant objec
Robotic applications involving people often require advanced perception systems to better understand complex real-world scenarios. To address this challenge, photo-realistic and physics simulators are gaining popularity as a means of generating accurate data labeling and designing scenarios for eval…
In this paper, we consider the problem where a drone has to collect semantic information to classify multiple moving targets. In particular, we address the challenge of computing control inputs that move the drone to informative viewpoints, position and orientation, when the information is extracted
In this paper, we present an automated parameter optimization method for trajectory generation. We formulate parameter optimization as a constrained optimization problem that can be effectively solved using Bayesian optimization. While the approach is generic to any trajectory generation method, we…
Many autonomous agents, such as intelligent vehicles, are inherently required to interact with one another. Game theory provides a natural mathematical tool for robot motion planning in such interactive settings. However, tractable algorithms for such problems usually rely on a strong assumption, na
Autonomous vehicles that operate in urban environments shall comply with existing rules and reason about the interactions with other decision-making agents. In this paper, we introduce a decentralized and communication-free interaction-aware motion planner and apply it to Autonomous Surface Vessels…
We study the problem of deploying a fleet of mobile robots to service tasks that arrive stochastically over time and at random locations in an environment. This is known as the Dynamic Vehicle Routing Problem (DVRP) and requires robots to allocate incoming tasks among themselves and find an optimal
Balancing safety and efficiency when planning in crowded scenarios with uncertain dynamics is challenging where it is imperative to accomplish the robot's mission without incurring any safety violations. Typically, chance constraints are incorporated into the planning problem to provide probabilisti…
Autonomous navigation of Micro Aerial Vehicles (MAVs) in dynamic and unknown environments is a complex and challenging task. Current works rely on assumptions to solve the problem. The MAV's pose is precisely known, the dynamic obstacles can be explicitly segmented from static ones, their number is
In this paper we propose a novel algorithm, Wi-Closure, to improve the computational efficiency and robustness of loop closure detection in multi-robot SLAM. Our approach decreases the computational overhead of classical approaches by pruning the search space of potential loop closures, prior to eva…
Reward learning is a highly active area of research in human-robot interaction (HRI), allowing a broad range of users to specify complex robot behaviour. Experiments with simulated user input play a major role in the development and evaluation of reward learning algorithms due to the availability of…
Autonomous mobile robots require accurate human motion predictions to safely and efficiently navigate among pedestrians, whose behavior may adapt to environmental changes. This letter introduces a self-supervised continual learning framework to improve data-driven pedestrian prediction models online
In multi-agent settings, game theory is a natural framework for describing the strategic interactions of agents whose objectives depend upon one another's behavior. Trajectory games capture these complex effects by design. In competitive settings, this makes them a more faithful interaction model th…
In unstructured urban canals, regulation-aware interactions with other vessels are essential for collision avoidance and social compliance. In this paper, we propose a regulations aware motion planning framework for Autonomous Surface Vessels (ASVs) that accounts for dynamic and static obstacles. Ou…
Search missions require motion planning and navigation methods for information gathering that continuously replan based on new observations of the robot's surroundings. Current methods for information gathering, such as Monte Carlo Tree Search, are capable of reasoning over long horizons, but they a…
This paper presents a real-time method for whole-body trajectory optimization of mobile manipulators in simplified dynamic and unstructured environments. Current trajectory optimization methods typically use decoupling of the mobile base and the robotic arm, which reduces flexibility in motion, does…
Multi-agent Pickup and Delivery (MAPD) is a challenging industrial problem where a team of robots is tasked with transporting a set of tasks, each from an initial location and each to a specified target location. Appearing in the context of automated warehouse logistics and automated mail sortation,
This letter presents a data-driven decentralized trajectory optimization approach for multi-robot motion planning in dynamic environments. When navigating in a shared space, each robot needs accurate motion predictions of neighboring robots to achieve predictive collision avoidance. These motion pre
We address the problem of assigning a team of drones to autonomously capture a set desired shots of a dynamic target in the presence of obstacles. We present a two-stage planning pipeline that generates offline an assignment of drone to shots and locally optimizes online the viewpoint. Given desired…
This paper presents an online informative path planning approach for active information gathering on three-dimensional surfaces using aerial robots. Most existing works on surface inspection focus on planning a path offline that can provide full coverage of the surface, which inherently assumes the…
We present an optimization-based method to plan the motion of an autonomous robot under the uncertainties associated with dynamic obstacles, such as humans. Our method bounds the marginal risk of collisions at each point in time by incorporating chance constraints into the planning problem. This pro
Robotic navigation in environments shared with other robots or humans remains challenging because the intentions of the surrounding agents are not directly observable and the environment conditions are continuously changing. Local trajectory optimization methods, such as model predictive control (MP
In this paper, we present an on-board vision-based approach for avoidance of moving obstacles in dynamic environments. Our approach relies on an efficient obstacle detection and tracking algorithm based on depth image pairs, which provides the estimated position, velocity and size of the obstacles.…
Prediction of human motions is key for safe navigation of autonomous robots among humans. In cluttered environments, several motion hypotheses may exist for a pedestrian, due to its interactions with the environment and other pedestrians. Previous works for estimating multiple motion hypotheses requ
Decentralized multi-robot systems typically perform coordinated motion planning by constantly broadcasting their intentions as a means to cope with the lack of a central system coordinating the efforts of all robots. Especially in complex dynamic environments, the coordination boost allowed by commu…
Safe autonomous navigation of microair vehicles in cluttered dynamic environments is challenging due to the uncertainties arising from robot localization, sensing, and motion disturbances. This letter presents a probabilistic collision avoidance method for navigation among other robots and moving ob
This paper presents a distributed method for splitting and merging of multi-robot formations in dynamic environments with static and moving obstacles. Splitting and merging actions rely on distributed consensus and can be performed to avoid obstacles. Our method accounts for the limited communicatio…
This letter presents a method for local motion planning in unstructured environments with static and moving obstacles, such as humans. Given a reference path and speed, our optimization-based receding-horizon approach computes a local trajectory that minimizes the tracking error while avoiding obsta
Mobility-on-demand (MoD) systems are revolutionizing urban transit with the introduction of ride-sharing. Such systems have the potential to reduce vehicle congestion and improve accessibility of a city's transportation infrastructure. Recently developed algorithms can compute routes for vehicles in…
When driving in urban environments, an autonomous vehicle must account for the interaction with other traffic participants. It must reason about their future behavior, how its actions affect their future behavior, and potentially consider multiple motion hypothesis. In this paper we introduce a meth…
Self-driving technology is expected to enable the realization of large-scale mobility-on-demand systems that employ massive ridesharing. The technology is being celebrated as a potential cure for urban congestion and others negative externalities of individual automobile transportation. In this pa…
In this letter, we propose an algorithm for the training of neural network control policies for quadrotors. The learned control policy computes control commands directly from sensor inputs and is, hence, computationally efficient. An imitation learning algorithm produces a policy that reproduces the
Recent developments in Mobility-on-Demand (MoD) systems have demonstrated the potential of road vehicles as an efficient mode of urban transportation Newly developed algorithms can compute vehicle routes in real-time for batches of requests and allow for multiple requests to share vehicles. These al…
Duckietown is an open, inexpensive and flexible platform for autonomy education and research. The platform comprises small autonomous vehicles (“Duckiebots”) built from off-the-shelf components, and cities (“Duckietowns”) complete with roads, signage, traffic lights, obstacles, and citizens (duckies…
Current state-of-the-art vehicle safety systems, such as assistive braking or automatic lane following, are still only able to help in relatively simple driving situations. We introduce a Parallel Autonomy shared-control framework that produces safe trajectories based on human inputs even in much mo…
Ride-sharing, or carpooling, systems with autonomous vehicles will provide efficient and reliable urban mobility on demand. In this work we present a method for dynamic vehicle routing that leverages historical data to improve the performance of a network of self-driving taxis. In particular, we des…
We propose a method for real-time trajectory generation with applications in aerial videography. Taking framing objectives, such as position of targets in the image plane, as input, our method solves for robot trajectories and gimbal controls automatically and adapts plans in real time due to change
When several Multirotor Micro Aerial Vehicles (MAVs) share the same airspace, reliable and robust collision avoidance is required. In this paper we address the problem of multi-MAV reactive collision avoidance. We employ a model-based controller to simultaneously track a reference trajectory and avo…
This paper presents a distributed method for navigating a team of robots in formation in 2D and 3D environments with static and dynamic obstacles. The robots are assumed to have a reduced communication and visibility radius and share information with their neighbors. Via distributed consensus the ro
This paper presents a formalism that exploits deformability during manipulation of soft objects by robot teams. A hybrid centralized/distributed approach restricts centralized planning to high-level global guidance of the object for consensus. Low-level control is thus delegated to the individual ma…
This paper presents a method for navigating a team of robots in formation in 2D and 3D environments with static and dynamic obstacles. The method is local and computes the optimal parameters for the formation within a neighborhood of the robots, allowing for reconfigurations, when required, by consi…