Author name cluster

Laura Ferranti

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

9 papers

2 author rows

TMLR Journal 2025 Journal Article

ASkDAgger: Active Skill-level Data Aggregation for Interactive Imitation Learning

Jelle Luijkx
Zlatan Ajanović
Laura Ferranti
Jens Kober

Human teaching effort is a significant bottleneck for the broader applicability of interactive imitation learning. To reduce the number of required queries, existing methods employ active learning to query the human teacher only in uncertain, risky, or novel situations. However, during these queries, the novice’s planned actions are not utilized despite containing valuable information, such as the novice’s capabilities, as well as corresponding uncertainty levels. To this end, we allow the novice to say: “I plan to do this, but I am uncertain.” We introduce the Active Skill-level Data Aggregation (ASkDAgger) framework, which leverages teacher feedback on the novice plan in three key ways: (1) S-Aware Gating (SAG): Adjusts the gating threshold to track sensitivity, specificity, or a minimum success rate; (2) Foresight Interactive Experience Replay (FIER), which recasts valid and relabeled novice action plans into demonstrations; and (3) Prioritized Interactive Experience Replay (PIER), which prioritizes replay based on uncertainty, novice success, and demonstration age. Together, these components balance query frequency with failure incidence, reduce the number of required demonstration annotations, improve generalization, and speed up adaptation to changing domains. We validate the effectiveness of ASkDAgger through language-conditioned manipulation tasks in both simulation and real-world environments. Code, data, and videos are available at https://askdagger.github.io.

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TMLR Journal 2025 Journal Article

REX: GPU-Accelerated Sim2Real Framework with Delay and Dynamics Estimation

Bas van der Heijden
Jens Kober
Robert Babuska
Laura Ferranti

Sim2real, the transfer of control policies from simulation to the real world, is crucial for efficiently solving robotic tasks without the risks associated with real-world learning. However, discrepancies between simulated and real environments, especially due to unmodeled dynamics and latencies, significantly impact the performance of these transferred policies. In this paper, we address the challenges of sim2real transfer caused by latency and asynchronous dynamics in real-world robotic systems. Our approach involves developing a novel framework, REX (Robotic Environments with jaX), that uses a graph-based simulation model to incorporate latency effects while optimizing for parallelization on accelerator hardware. Our framework simulates the asynchronous, hierarchical nature of real-world systems, while simultaneously estimating system dynamics and delays from real-world data and implementing delay compensation strategies to minimize the sim2real gap. We validate our approach on two real-world systems, demonstrating its effectiveness in improving sim2real performance by accurately modeling both system dynamics and delays. Our results show that the proposed framework supports both accelerated simulation and real-time processing, making it valuable for robot learning.

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TMLR Journal 2024 Journal Article

Efficient Parallelized Simulation of Cyber-Physical Systems

Bas van der Heijden
Laura Ferranti
Jens Kober
Robert Babuska

Advancements in accelerated physics simulations have greatly reduced training times for reinforcement learning policies, yet the conventional step-by-step agent-simulator interaction undermines simulation accuracy. In the real-world, interactions are asynchronous, with sensing, acting and processing happening simultaneously. Failing to capture this widens the sim2real gap and results in suboptimal real-world performance. In this paper, we address the challenges of simulating realistic asynchronicity and delays within parallelized simulations, crucial to bridging the sim2real gap in complex cyber-physical systems. Our approach efficiently parallelizes cyber-physical system simulations on accelerator hardware, including physics, sensors, actuators, processing components and their asynchronous interactions. We extend existing accelerated physics simulations with latency simulation capabilities by constructing a `supergraph' that encodes all data dependencies across parallelized simulation steps, ensuring accurate simulation. By finding the smallest supergraph, we minimize redundant computation. We validate our approach on two real-world systems and perform an extensive ablation, demonstrating superior performance compared to baseline methods.

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ICRA Conference 2024 Conference Paper

Probabilistic Motion Planning and Prediction via Partitioned Scenario Replay

Oscar de Groot
Anish Sridharan
Javier Alonso-Mora
Laura Ferranti

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 information on the pedestrian’s behavior, but may deviate from the data, which can lead to collisions during planning. In this work, we introduce a joint prediction and planning framework, Partitioned Scenario Replay (PSR), that stores and partitions previously observed human trajectories, referred to as scenarios. During planning, scenarios observed in similar situations are reintroduced (or replayed) as motion predictions. By sampling real data and by building on scenario optimization and predictive control, the planner provides probabilistic collision avoidance guarantees in the real-world. Relying on this guarantee to remain safe, PSR can incrementally improve its prediction and planning performance online. We demonstrate our approach on a mobile robot navigating around pedestrians.

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ICRA Conference 2023 Conference Paper

Curvature-Aware Model Predictive Contouring Control

Lorenzo Lyons
Laura Ferranti

We present a novel Curvature-Aware Model Pre-dictive Contouring Control (CA-MPCC) formulation for mobile robotics motion planning. Our method aims at generalizing the traditional contouring control formulation derived from machining to autonomous driving applications. The proposed controller is able of handling sharp curvatures in the reference path while subject to non-linear constraints, such as lane boundaries and dynamic obstacle collision avoidance.Com-pared to a standard MPCC formulation, our method improves the reliability of the path-following algorithm and simplifies the tuning, while preserving real-time capabilities. We validate our findings in both simulations and experiments on a scaled-down car-like robot.

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ICRA Conference 2023 Conference Paper

Globally Guided Trajectory Planning in Dynamic Environments

Oscar de Groot
Laura Ferranti
Dariu M. Gavrila
Javier Alonso-Mora

Navigating mobile robots through environments shared with humans is challenging. From the perspective of the robot, humans are dynamic obstacles that must be avoided. These obstacles make the collision-free space nonconvex, which leads to two distinct passing behaviors per obstacle (passing left or right). For local planners, such as receding-horizon trajectory optimization, each behavior presents a local optimum in which the planner can get stuck. This may result in slow or unsafe motion even when a better plan exists. In this work, we identify trajectories for multiple locally optimal driving behaviors, by considering their topology. This identification is made consistent over successive iterations by propagating the topology information. The most suitable high-level trajectory guides a local optimization-based planner, resulting in fast and safe motion plans. We validate the proposed planner on a mobile robot in simulation and real-world experiments.

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ICRA Conference 2022 Conference Paper

Where to Look Next: Learning Viewpoint Recommendations for Informative Trajectory Planning

Max Lodel
Bruno Brito
Álvaro Serra-Gómez
Laura Ferranti
Robert Babuska
Javier Alonso-Mora

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 are computationally expensive. An alternative for fast online execution is to train, offline, an information gathering policy, which indirectly reasons about the information value of new observations. However, these policies lack safety guarantees and do not account for the robot dynamics. To overcome these limitations we train an information-aware policy via deep reinforcement learning, that guides a receding-horizon trajectory optimization planner. In particular, the policy continuously recommends a reference viewpoint to the local planner, such that the resulting dynamically feasible and collision-free trajectories lead to observations that maximize the information gain and reduce the uncertainty about the environment. In simulation tests in previously unseen environments, our method consistently outperforms greedy next-best-view policies and achieves competitive performance compared to Monte Carlo Tree Search, in terms of information gains and coverage time, with a reduction in execution time by three orders of magnitude.

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IROS Conference 2021 Conference Paper

DeepKoCo: Efficient latent planning with a task-relevant Koopman representation

Bas van der Heijden
Laura Ferranti
Jens Kober
Robert Babuska

This paper presents DeepKoCo, a novel modelbased agent that learns a latent Koopman representation from images. This representation allows DeepKoCo to plan efficiently using linear control methods, such as linear model predictive control. Compared to traditional agents, DeepKoCo learns taskrelevant dynamics, thanks to the use of a tailored lossy autoencoder network that allows DeepKoCo to learn latent dynamics that reconstruct and predict only observed costs, rather than all observed dynamics. As our results show, DeepKoCo achieves a similar final performance as traditional model-free methods on complex control tasks, while being considerably more robust to distractor dynamics, making the proposed agent more amenable for real-life applications.

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ICRA Conference 2019 Conference Paper

Distributed Multi-Robot Formation Splitting and Merging in Dynamic Environments

Hai Zhu 0002
Jelle Juhl
Laura Ferranti
Javier Alonso-Mora

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 communication range and visibility radius of the robots and relies on the communication of obstacle-free convex regions and the computation of an intersection graph. In addition, our method is able to detect and recover from (permanent and temporary) communication and motion faults. Finally, we demonstrate the applicability and scalability of the proposed method in simulations with up to sixteen quadrotors and real-world experiments with a team of four quadrotors.

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