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Safe Multi-Agent Reinforcement Learning for Behavior-Based Cooperative Navigation

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IEEE Robotics and Automation Letters 2025年第6期10卷 6256-6263页

作者： Dawood, Murad Pan, Sicong Dengler, Nils Zhou, Siqi Schoellig, Angela P. Bennewitz, Maren University of Bonn Humanoid Robots Lab Germany Center for Robotics Lamarr Institute for Machine Learning and Artificial Intelligence Bonn Germany The Technical University of Munich Learning Systems and Robotics lab Germany

In this paper, we address the problem of behavior-based cooperative navigation of mobile robots using safe multi-agent reinforcement learning (MARL). Our work is the first to focus on cooperative navigation without individual reference targets for the robots, using a single target for the formation's centroid. This eliminates the complexities involved in having several path planners to control a team of robots. To ensure safety, our MARL framework uses model predictive control (MPC) to prevent actions that could lead to collisions during training and execution. We demonstrate the effectiveness of our method in simulation and on real robots, achieving safe behavior-based cooperative navigation without using individual reference targets, with zero collisions, and faster target reaching compared to baselines. Finally, we study the impact of MPC safety filters on the learning process, revealing that we achieve faster convergence during training and we show that our approach can be safely deployed on real robots, even during early stages of the training. © 2016 IEEE.

关键词： Adversarial machine learning

Extended Swarming with Embodied Neural Computation for Human Control over Swarms 17th

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Extended Swarming with Embodied Neural Computation for Human...

17th International Conference on Simulation of Adaptive Behavior (SAB)

作者： Rockbach, Jonas D. Bennewitz, Maren Fraunhofer FKIE Human Machine Syst D-53343 Wachtberg Germany Univ Bonn Humanoid Robots Lab D-53113 Bonn Germany

ISBN: (纸本)9783031715327;9783031715334

With the rise of robot swarms, it has become a relevant problem how humans can control them. Extended swarming is a potential approach in which robot swarms are treated as self-organising extensions of human bodies. Swarm control takes the form of controlling the observable swarm body while robot chains connect the human operator to relevant aspects of the environment. Inspired by how natural bodies are controlled by a nervous system, we here investigate how the swarm body's self-organisation can be influenced by robot chains acting as embodied neural traces while remaining under human high-level control. Three design principles are proposed for such embodied neural computation. First, the swarm body's self-organisation is controlled both by top-down human control and bottom-up sensor inputs alike to the hierarchical control architecture of the nervous system. Second, robots participating in robot chains are treated as rate-coded neurons rendering the chains as embodied neural traces which offers intuitive control possibilities for the human. Third, neural and swarm self-organisation are integrated by utilizing the swarm's communication network as a scaffolding for neural function influencing swarm dynamics. This process is interpreted as embodied Hebbian learning. Human control over the swarm is demonstrated in a grid-based search-and-rescue simulation with the objective of selecting the most valuable subregion defined by accumulated victims in need. We evaluate how using embodied trace relevance in terms of neural activation improves completion time to finding the highest-value trace as well as how attracting units to relevant traces increases their robustness.

关键词： Human-swarm interaction Extended swarming Swarm robotics Embodied neural computation Bio-inspiration

Evaluating Robot Influence on Pedestrian Behavior Models for Crowd Simulation and Benchmarking 16th

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Evaluating Robot Influence on Pedestrian Behavior Models fo...

16th International Conference on Social Robotics, ICSR + AI 2024

作者： Agrawal, Subham Dengler, Nils Bennewitz, Maren Humanoid Robots Lab University of Bonn Bonn Germany The Lamarr Institute Bonn Germany Center for Robotics Bonn Germany

ISBN: (纸本)9789819635184

The presence of robots amongst pedestrians affects them causing deviation to their trajectories. Existing methods suffer from the limitation of not being able to objectively measure this deviation in unseen cases. In order to solve this issue, we introduce a simulation framework that repetitively measures and benchmarks the deviation in trajectory of pedestrians due to robots driven by different navigation algorithms. We simulate the deviation behavior of the pedestrians using an enhanced Social Force Model (SFM) with a robot force component that accounts for the influence of robots on pedestrian behavior, resulting in the Social Robot Force Model (SRFM). Parameters for this model are learned using the pedestrian trajectories from the JRDB dataset [1]. Pedestrians are then simulated using the SRFM with and without the robot force component to objectively measure the deviation to their trajectory caused by the robot in 5 different scenarios. Our work in this paper is a proof of concept that shows objectively measuring the pedestrian reaction to robot is possible. We use our simulation to train two different RL policies and evaluate them against traditional navigation models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Trajectories

A tactile perception method with flexible grating structural color

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NATIONAL SCIENCE REVIEW 2025年第1期12卷 nwae413页

作者： Qiu, Yuze Yan, Chunfei Zhang, Yan Yang, Shengxuan Yao, Xiang Ai, Fawen Zheng, Jinjin Zhang, Shiwu Yu, Xinge Dong, Erbao Univ Sci & Technol China Key Lab Precis & Intelligent Chem Hefei 230026 Peoples R China Univ Sci & Technol China Inst Humanoid Robots Dept Precis Machinery & Precis Instrumentat Hefei 230026 Peoples R China City Univ Hong Kong Dept Biomed Engn Hong Kong 999077 Peoples R China

Affordable high-resolution cameras and state-of-the-art computer vision techniques have led to the emergence of various vision-based tactile sensors. However, current vision-based tactile sensors mainly depend on geometric optics or marker tracking for tactile assessments, resulting in limited performance. To solve this dilemma, we introduce optical interference patterns as the visual representation of tactile information for flexible tactile sensors. We propose a novel tactile perception method and its corresponding sensor, combining structural colors from flexible blazed gratings with deep learning. The richer structural colors and finer data processing foster the tactile estimation performance. The proposed sensor has an overall normal force magnitude accuracy of 6 mN, a planar resolution of 79 mu m and a contact-depth resolution of 25 mu m. This work presents a promising tactile method that combines wave optics, soft materials and machine learning. It performs well in tactile measurement, and can be expanded into multiple sensing fields. This work introduces a novel tactile perception method that merges wave optics, soft materials, and machine learning, dramatically improving sensor tactile performance and ushering in new advancements in tactile technology.

关键词： force and tactile sensing structural color vision-based tactile sensor deep learning wave optics

EvidMTL: Evidential Multi-Task Learning for Uncertainty-Aware Semantic Surface Mapping from Monocular RGB Images

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arXiv 2025年

作者： Menon, Rohit Dengler, Nils Pan, Sicong Chenchani, Gokul Krishna Bennewitz, Maren Humanoid Robots Lab Center for Robotics University of Bonn Germany Lamarr Institute Bonn Germany

For scene understanding in unstructured environments, an accurate and uncertainty-aware metric-semantic mapping is required to enable informed action selection by autonomous systems. Existing mapping methods often suffer from overconfident semantic predictions, and sparse and noisy depth sensing, leading to inconsistent map representations. In this paper, we therefore introduce EvidMTL, a multi-task learning framework that uses evidential heads for depth estimation and semantic segmentation, enabling uncertainty-aware inference from monocular RGB images. To enable uncertainty-calibrated evidential multi-task learning, we propose a novel evidential depth loss function that jointly optimizes the belief strength of the depth prediction in conjunction with evidential segmentation loss. Building on this, we present EvidKimera, an uncertainty-aware semantic surface mapping framework, which uses evidential depth and semantics prediction for improved 3D metric-semantic consistency. We train and evaluate EvidMTL on the NYUDepthV2 and assess its zero-shot performance on ScanNetV2, demonstrating superior uncertainty estimation compared to conventional approaches while maintaining comparable depth estimation and semantic segmentation. In zero-shot mapping tests on ScanNetV2, EvidKimera outperforms Kimera in semantic surface mapping accuracy and consistency, highlighting the benefits of uncertainty-aware mapping and underscoring its potential for real-world robotic applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

Pedestrians and robots: A Novel Dataset for Learning Distinct Social Navigation Forces

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arXiv 2025年

作者： Agrawal, Subham Ostermann-Myrau, Nico Dengler, Nils Bennewitz, Maren Humanoid Robots Lab University of Bonn Germany Lamarr Institute for Machine Learning and Artificial Intelligence The Center for Robotics Bonn Germany

The increasing use of robots in human-centric public spaces such as shopping malls, sidewalks, and hospitals, requires understanding of how pedestrians respond to their presence. However, existing research lacks comprehensive datasets that capture the full range of pedestrian behaviors, e.g., including avoidance, neutrality, and attraction in the presence of robots. Such datasets can be used to effectively learn models capable of accurately predicting diverse responses of pedestrians to robot presence, which are crucial for advancing robot navigation strategies and optimizing pedestrian-aware motion planning. In this paper, we address these challenges by collecting a novel dataset of pedestrian motion in two outdoor locations under three distinct conditions, i.e., no robot presence, a stationary robot, and a moving robot. Thus, unlike existing datasets, ours explicitly encapsulates variations in pedestrian behavior across the different robot conditions. Using our dataset, we propose a novel Neural Social Robot Force Model (NSRFM), an extension of the traditional Social Force Model that integrates neural networks and robot-induced forces to better predict pedestrian behavior in the presence of robots. We validate the NSRFM by comparing its generated trajectories on different real-world datasets. Furthermore, we implemented it in simulation to enable the learning and benchmarking of robot navigation strategies based on their impact on pedestrian movement. Our results demonstrate the model’s effectiveness in replicating real-world pedestrian reactions and its its utility in developing, evaluating, and benchmarking social robot navigation algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Social robots

GO-VMP: Global Optimization for View Motion Planning in Fruit Mapping

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arXiv 2025年

作者： Jose, Allen Isaac Pan, Sicong Zaenker, Tobias Menon, Rohit Houben, Sebastian Bennewitz, Maren Bonn-Rhein-Sieg University of Applied Sciences Germany Humanoid Robots Lab University of Bonn Germany Fraunhofer Institute for Intelligent Analysis and Information Systems Germany Humanoid Robots Lab University of Bonn Lamarr Institute for Machine Learning and Artificial Intelligence Center for Robotics University of Bonn Germany

Automating labor-intensive tasks such as crop monitoring with robots is essential for enhancing production and conserving resources. However, autonomously monitoring horticulture crops remains challenging due to their complex structures, which often result in fruit occlusions. Existing view planning methods attempt to reduce occlusions but either struggle to achieve adequate coverage or incur high robot motion costs. We introduce a global optimization approach for view motion planning that aims to minimize robot motion costs while maximizing fruit coverage. To this end, we leverage coverage constraints derived from the set covering problem (SCP) within a shortest Hamiltonian path problem (SHPP) formulation. While both SCP and SHPP are well-established, their tailored integration enables a unified framework that computes a global view path with minimized motion while ensuring full coverage of selected targets. Given the NP-hard nature of the problem, we employ a region-prior-based selection of coverage targets and a sparse graph structure to achieve effective optimization outcomes within a limited time. Experiments in simulation demonstrate that our method detects more fruits, enhances surface coverage, and achieves higher volume accuracy than the motion-efficient baseline with a moderate increase in motion cost, while significantly reducing motion costs compared to the coverage-focused baseline. Real-world experiments further confirm the practical applicability of our approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Fruits

Regulating Topographical Deformation Response of Liquid Crystal Coatings by Electrical Quenching of Liquid Crystal Order Fluctuation

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SMALL STRUCTURES

SMALL STRUCTURES 2025年

作者： Feng, Wei Univ Sci & Technol China Inst Humanoid Robots Dept Modern Mech CAS Key Lab Mech Behav & Design Mat Hefei 230027 Peoples R China

Alignment of liquid crystals (LCs) is critical for actuation of LC polymer-based devices. A common strategy to induce surface deformation in LC polymer coatings involves engineering the polymers into multidomain configurations. The application of an electric field serves as an efficient means to achieve this goal. While LCs are partially ordered fluids characterized by intrinsic dynamic disorder and order fluctuations, these fluctuations can be suppressed by electric fields, a phenomenon known as electrical quenching. However, the impact of quenching LC order fluctuations on the molecular actuation behavior of LC polymers remains insufficiently understood. Herein, the translation of the electric quenching effect on LC order from the molecular scale to the macroscopic level, with a focus on regulating the topographical deformation of polymerized LC polymer coatings, is demonstrated. The findings reveal that the suppression of monomeric LC order fluctuations through electric quenching significantly reduces the topographical deformation of the polymerized network. This advancement offers new opportunities for fabricating tunable surfaces with complex patterns, broadening the potential applications of LC-based dynamic surfaces.

关键词： liquid crystal alignments liquid crystal polymers order fluctuation quenching surface topography

Map Space Belief Prediction for Manipulation-Enhanced Mapping

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arXiv 2025年

作者： Marques, Joao Marcos Correia Dengler, Nils Zaenker, Tobias Mücke, Jesper Wang, Shenlong Bennewitz, Maren Hauser, Kris University of Illinois at Urbana-Champaign IL United States Humanoid Robots Lab University of Bonn Germany The Lamarr Institute Bonn Germany The Center for Robotics University of Bonn Germany

Searching for objects in cluttered environments requires selecting efficient viewpoints and manipulation actions to remove occlusions and reduce uncertainty in object locations, shapes, and categories. In this work, we address the problem of manipulation-enhanced semantic mapping, where a robot has to efficiently identify all objects in a cluttered shelf. Although Partially Observable Markov Decision Processes (POMDPs) are standard for decision-making under uncertainty, representing unstructured interactive worlds remains challenging in this formalism. To tackle this, we define a POMDP whose belief is summarized by a metric-semantic grid map and propose a novel framework that uses neural networks to perform map-space belief updates to reason efficiently and simultaneously about object geometries, locations, categories, occlusions, and manipulation physics. Further, to enable accurate information gain analysis, the learned belief updates should maintain calibrated estimates of uncertainty. Therefore, we propose Calibrated Neural-Accelerated Belief Updates (CNABUs) to learn a belief propagation model that generalizes to novel scenarios and provides confidence-calibrated predictions for unknown areas. Our experiments show that our novel POMDP planner improves map completeness and accuracy over existing methods in challenging simulations and successfully transfers to real-world cluttered shelves in zero-shot fashion. Copyright © 2025, The Authors. All rights reserved.

关键词： Markov processes