HARMONY: Assistive Robots for Healthcare

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Luzia Knoedler - PhD candidate
Dr. Nils Wilde - Postdoctoral researcher
Prof. Javier Alonso-Mora - Autonomous Multi-Robot Lab (AMR) TU Delft


This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101017008.

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About the Project

TU Delft partners

Harmony is a Horizon 2020 project which develops assistive robotic mobile manipulation technologies for environments shared with humans, i.e., hospitals. Specifically, Harmony addresses two use cases:

  1. The automation of on-demand delivery tasks around the hospital
  2. The automation of bio-assay sample flow

Current robotic automation solutions only offer “islands of automation” where either mobility or manipulation is dealt with in isolation. Harmony aims to fill this gap in knowledge by combining both robotic mobility and manipulation modalities in complex, human-centred environments. We at AMR focus on providing adaptive task and motion planning with multiple robots in human-centred environments.

Traditionally task scheduling and planning has been decoupled from motion planning. Yet, when robots navigate in critical and dynamic environments, plans may have to be adapted online to take into account congestion and interaction with other robots and human co-workers. We will devise methods for multi-robot motion planning that schedule plans for the robots and adapt them online taking into account the priority of tasks, their associated uncertainty and the preferences and needs of human co-workers. Furthermore, we will develop local-motion planning approaches which do not only account for collision avoidance but also consider social interactions.

The goal is to have an integrated approach for mobile manipulation in uncertain and dynamic environments shared with humans, which accounts for social interactions, navigation and coordination tasks, and that provides performance guarantees (in expectation).


TU Delft partners

Related Publications

Statistically Distinct Plans for Multi-Objective Task Assignment
Nils Wilde, Javier Alonso-Mora. In IEEE Transaction on Robotics(T-RO), 2024.

We study the problem of finding statistically distinct plans for stochastic task assignment problems such as online multi-robot pickup and delivery (MRPD) when facing multiple competing objectives. In many real-world settings robot fleets do not only need to fulfil delivery requests, but also have to consider auxiliary objectives such as energy efficiency or avoiding human-centered work spaces. We pose MRPD as a multi-objective optimization problem where the goal is to find MRPD policies that yield different trade-offs between given objectives. There are two main challenges: 1) MRPD is computationally hard, which limits the number of trade-offs that can reasonably be computed, and 2) due to the random task arrivals, one needs to consider statistical variance of the objective values in addition to the average. We present an adaptive sampling algorithm that finds a set of policies which i) are approximately optimal, ii) approximate the set of all optimal solutions, and iii) are statistically distinguishable. We prove completeness and adapt a state-of-the-art MRPD solver to the multi-objective setting for three example objectives. In a series of simulation experiments we demonstrate the advantages of the proposed method compared to baseline approaches and show its robustness in a sensitivity analysis. The approach is general and could be adapted to other multi-objective task assignment and planning problems under uncertainty.

Scalarizing Multi-Objective Robot Planning Problems Using Weighted Maximization
Nils Wilde, Stephen L. Smith, Javier Alonso-Mora. In IEEE Robotics and Automation Letters (RA-L), 2024.

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 objective functions and then carefully adapt the weights. However, this approach may not find all relevant trade-offs even in simple planning problems. Thus, we study an alternative method based on a weighted maximum of objectives. Such a cost function is more expressive than the weighted sum, and we show how it can be deployed in both continuous- and discrete-space motion planning problems. We propose a novel path planning algorithm for the proposed cost function and establish its correctness, and present heuristic adaptations that yield a practical runtime. In extensive simulation experiments, we demonstrate that the proposed cost function and algorithm are able to find a wider range of trade-offs between objectives (i.e., Pareto-optimal solutions) for various planning problems, showcasing its advantages in practice.

Optimizing Task Waiting Times in Dynamic Vehicle Routing
A. Botros, B. Gilhuly, N. Wilde, A. Sadeghi, J. Alonso-Mora, S. L Smith. In IEEE Robotics and Automation Letters (RA-L), 2023.

Abstract: 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 sequence for each robot. State-of-the-art approaches only consider average wait times and focus on high-load scenarios where the arrival rate of tasks approaches the limit of what can be handled by the robots while keeping the queue of unserviced tasks bounded, i.e., stable. To ensure stability, these approaches repeatedly compute minimum distance tours over a set of newly arrived tasks. This letter is aimed at addressing the missing policies for moderate-load scenarios, where quality of service can be improved by prioritizing long-waiting tasks. We introduce a novel DVRP policy based on a cost function that takes the p-norm over accumulated wait times and show it guarantees stability even in high-load scenarios. We demonstrate that the proposed policy outperforms the state-of-the-art in both mean and 95th percentile wait times in moderate-load scenarios through simulation experiments in the Euclidean plane as well as using real-world data for city scale service requests.

Designing Heterogeneous Robot Fleets for Task Allocation and Sequencing
N. Wilde, J. Alonso-Mora. In Proc. IEEE International Symposium on Multi-Robot and Multi-Agent Systems (MRS), 2023.

Abstract: We study the problem of selecting a fleet of robots to service spatially distributed tasks with diverse requirements within time-windows. The problem of allocating tasks to a fleet of potentially heterogeneous robots and finding an optimal sequence for each robot is known as multi-robot task assignment (MRTA). Most state-of-the-art methods focus on the problem when the fleet of robots is fixed. In contrast, we consider that we are given a set of available robot types and requested tasks, and need to assemble a fleet that optimally services the tasks while the cost of the fleet remains under a budget limit. We characterize the complexity of the problem and provide a Mixed-Integer Linear Program (MILP) formulation. Due to poor scalability of the MILP, we propose a heuristic solution based on a Large Neighbourhood Search (LNS). In simulations, we demonstrate that the proposed method requires substantially lower budgets than a greedy algorithm to service all tasks.

Multi-Robot Local Motion Planning Using Dynamic Optimization Fabrics
S. Bakker, L. Knoedler, M. Spahn, W. Böhmer, J. Alonso-Mora. In Proc. IEEE International Symposium on Multi-Robot and Multi-Agent Systems, 2023.

Abstract: In this paper, we address the problem of real-time motion planning for multiple robotic manipulators that operate in close proximity. We build upon the concept of dynamic fabrics and extend them to multi-robot systems, referred to as Multi-Robot Dynamic Fabrics (MRDF). This geometric method enables a very high planning frequency for high-dimensional systems at the expense of being reactive and prone to deadlocks. To detect and resolve deadlocks, we propose Rollout Fabrics where MRDF are forward simulated in a decentralized manner. We validate the methods in simulated close-proximity pick-and-place scenarios with multiple manipulators, showing high success rates and real-time performance.

Group-based Distributed Auction Algorithms for Multi-Robot Task Assignment
X. Bai, A. Fielbaum, M. Kronmuller, L. Knoedler, J. Alonso-Mora. In IEEE Transactions on Automation Science and Engineering (T-ASE), 2022.

Abstract: This paper studies the multi-robot task assignment problem in which a fleet of dispersed robots needs to efficiently transport a set of dynamically appearing packages from their initial locations to corresponding destinations within prescribed time-windows. Each robot can carry multiple packages simultaneously within its capacity. Given a sufficiently large robot fleet, the objective is to minimize the robots' total travel time to transport the packages within their respective time-window constraints. The problem is shown to be NP-hard, and we design two group-based distributed auction algorithms to solve this task assignment problem. Guided by the auction algorithms, robots first distributively calculate feasible package groups that they can serve, and then communicate to find an assignment of package groups. We quantify the potential of the algorithms with respect to the number of employed robots and the capacity of the robots by considering the robots' total travel time to transport all packages. Simulation results show that the designed algorithms are competitive compared with an exact centralized Integer Linear Program representation solved with the commercial solver Gurobi, and superior to popular greedy algorithms and a heuristic distributed task allocation method.

Improving Pedestrian Prediction Models with Self-Supervised Continual Learning
L. Knoedler, C. Salmi, H. Zhu, B. Brito, J. Alonso-Mora. In , IEEE Robotics and Automation Letters (RA-L), 2022.

Abstract: 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 across various scenarios continuously. In particular, we exploit online streams of pedestrian data, commonly available from the robot’s detection and tracking pipeline, to refine the prediction model and its performance in unseen scenarios. To avoid the forgetting of previously learned concepts, a problem known as catastrophic forgetting, our framework includes a regularization loss to penalize changes of model parameters that are important for previous scenarios and retrains on a set of previous examples to retain past knowledge. Experimental results on real and simulation data show that our approach can improve prediction performance in unseen scenarios while retaining knowledge from seen scenarios when compared to naively training the prediction model online.

Do we use the Right Measure? Challenges in Evaluating Reward Learning Algorithms
N. Wilde, J. Alonso-Mora. In Conference on Robot Learning (CoRL), 2022.

Abstract: Reward learning is a highly active area of research in human-robot interaction (HRI), allowing a broad range of users to specify complex robot be- haviour. Experiments with simulated user input play a major role in the devel- opment and evaluation of reward learning algorithms due to the availability of a ground truth. In this paper, we review measures for evaluating reward learning algorithms used in HRI, most of which fall into two classes. In a theoretical worst case analysis and several examples, we show that both classes of measures can fail to effectively indicate how good the learned robot behaviour is. Thus, our work contributes to the characterization of sim-to-real gaps of reward learning in HRI.

Online Multi-Robot Task Assignment with Stochastic Blockages
N. Wilde, J. Alonso-Mora. In IEEE Conference on Decision and Control (CDC), 2022.

Abstract: In this paper we study the multi-robot task assignment problem with tasks that appear online and need to be serviced within a fixed time window in an uncertain environment. For example, when deployed in dynamic, human centered environments, the team of robots may not have perfect information about the environment. Parts of the environment may temporarily become blocked and blockages may only be observed on location. While numerous variants of the Canadian Traveler Problem describe the path planning aspect of this problem, few work has been done on multi-robot task allocation (MRTA) under this type of uncertainty. In this paper, we introduce and theoretically analyze the problem of MRTA with recoverable online blockages. Based on a stochastic blockage model, we compute offline tours using the expected travel costs for the online routing problem. The cost of the offline tours is used in a greedy task assignment algorithm. In simulation experiments we highlight the performance benefits of the proposed method under various settings.

Error-Bounded Approximation of Pareto Fronts in Robot Planning Problems
A. Botros, A. Sadeghi, N. Wilde, J. Alonso-Mora, S. L. Smith. In 15th Workshop on the Algorithmic Foundations of Robotics (WAFR), 2022.

Abstract: Many problems in robotics seek to simultaneously optimize several competing objectives under constraints. A conventional approach to solving such multi-objective optimization problems is to create a single cost function comprised of the weighted sum of the individual objectives. Solutions to this scalarized optimization problem are Pareto optimal solutions to the original multi-objective problem. However, finding an accurate representation of a Pareto front remains an important challenge. Using uniformly spaced weight vectors is often inefficient and does not provide error bounds. Thus, we address the problem of computing a finite set of weight vectors such that for any other weight vector, there exists an element in the set whose error compared to optimal is minimized. To this end, we prove fundamental properties of the optimal cost as a function of the weight vector, including its continuity and concavity. Using these, we propose an algorithm that greedily adds the weight vector least-represented by the current set, and provide bounds on the error. Finally, we illustrate that the proposed approach significantly outperforms uniformly distributed weights for different robot planning problems with varying numbers of objective functions.

Where to go next: Learning a Subgoal Recommendation Policy for Navigation in Dynamic Environments
B. Brito, M. Everett, J. P. How, J. Alonso-Mora. In , IEEE Robotics and Automation Letters (RA-L), 2021.

Abstract: 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 (MPC), can deal with those changes but require global guidance, which is not trivial to obtain in crowded scenarios. This letter proposes to learn, via deep Reinforcement Learning (RL), an interaction-aware policy that provides long-term guidance to the local planner. In particular, in simulations with cooperative and non-cooperative agents, we train a deep network to recommend a subgoal for the MPC planner. The recommended subgoal is expected to help the robot in making progress towards its goal and accounts for the expected interaction with other agents. Based on the recommended subgoal, the MPC planner then optimizes the inputs for the robot satisfying its kinodynamic and collision avoidance constraints. Our approach is shown to substantially improve the navigation performance in terms of number of collisions as compared to prior MPC frameworks, and in terms of both travel time and number of collisions compared to deep RL methods in cooperative, competitive and mixed multiagent scenarios.