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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

Compact Multi-Object Placement Using Adjacency-Aware Reinforcement Learning 23

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Compact Multi-Object Placement Using Adjacency-Aware Reinfor...

23rd IEEE-RAS International Conference on humanoid robots, humanoids 2024

作者： Kreis, Benedikt Dengler, Nils Heuvel, Jorge De Menon, Rohit Perur, Hamsa Bennewitz, Maren University of Bonn Humanoid Robots Lab Germany University of Bonn Center for Robotics Germany Lamarr Institute for Ml and Ai Bonn Germany

ISBN: (纸本)9798350373578

Close and precise placement of irregularly shaped objects requires a skilled robotic system. The manipulation of objects that have sensitive top surfaces and a fixed set of neighbors is particularly challenging. To avoid damaging the surface, the robot has to grasp them from the side, and during placement, it has to maintain the spatial relations with adjacent objects, while considering the physical gripper extent. In this work, we propose a framework to learn an agent based on reinforcement learning that generates end-effector motions for placing objects as closely as possible to one another. During the placement, our agent considers the spatial constraints with neighbors defined in a given layout of the objects while avoiding collisions. Our approach learns to place compact object assemblies without the need for predefined spacing between objects, as required by traditional methods. We thoroughly evaluated our approach using a two-finger gripper mounted on a robotic arm with six degrees of freedom. The results demonstrate that our agent significantly outperforms two baseline approaches in object assembly compactness, thereby reducing the space required to position the objects while adhering to specified spatial constraints. © 2024 IEEE.

关键词： Reinforcement learning

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

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

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arXiv 2024年

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

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. Copyright © 2024, The Authors. All rights reserved.

关键词： Trajectories

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

Learning Adaptive Multi-Objective Robot Navigation Incorporating Demonstrations

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arXiv 2024年

作者： Heuvel, Jorge de Sethuraman, Tharun Bennewitz, Maren The Humanoid Robots Lab University of Bonn Germany The Lamarr Institute for Machine Learning and Artificial Intelligence The Center for Robotics Bonn Germany

Preference-aligned robot navigation in human environments is typically achieved through learning-based approaches, utilizing user feedback or demonstrations for personalization. However, personal preferences are subject to change and might even be context-dependent. Yet traditional reinforcement learning (RL) approaches with static reward functions often fall short in adapting to these varying user preferences, inevitably reflecting demonstrations once training is completed. This paper introduces a framework that combines multiobjective reinforcement learning (MORL) with demonstration-based learning. Our approach allows for dynamic adaptation to changing user preferences without retraining. It fluently modulates between reward-defined preference objectives and the amount of demonstration data reflection. Through rigorous evaluations, including a sim-to-real transfer on two robots, we demonstrate our framework’s capability to reflect user preferences accurately while achieving high navigational performance in terms of collision avoidance and goal pursuance. © 2024, CC BY.

关键词： Adversarial machine learning

EnQuery: Ensemble Policies for Diverse Query-Generation in Preference Alignment of Robot Navigation

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arXiv 2024年

作者： de Heuvel, Jorge Seiler, Florian Bennewitz, Maren The Humanoid Robots Lab The Center for Robotics The University of Bonn Germany The Lamarr Institute for Machine Learning and Artificial Intelligence Bonn Germany

To align mobile robot navigation policies with user preferences through reinforcement learning from human feedback (RLHF), reliable and behavior-diverse user queries are required. However, deterministic policies fail to generate a variety of navigation trajectory suggestions for a given navigation task. In this paper, we introduce EnQuery, a query generation approach using an ensemble of policies that achieve behavioral diversity through a regularization term. For a given navigation task, EnQuery produces multiple navigation trajectory suggestions, thereby optimizing the efficiency of preference data collection with fewer queries. Our methodology demonstrates superior performance in aligning navigation policies with user preferences in low-query regimes, offering enhanced policy convergence from sparse preference queries. The evaluation is complemented with a novel explainability representation, capturing full scene navigation behavior of the mobile robot in a single plot. Our code is available online at https://***/hrl-bonn/EnQuery. © 2024, CC BY.

关键词： Navigation

EnQuery: Ensemble Policies for Diverse Query-Generation in Preference Alignment of Robot Navigation

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EnQuery: Ensemble Policies for Diverse Query-Generation in P...

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Jorge De Heuvel Florian Seiler Maren Bennewitz Humanoid Robots Lab and the Center for Robotics University of Bonn Germany Lamarr Institute for Machine Learning and Artificial Intelligence Bonn Germany

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

关键词： Training Navigation Data visualization Reinforcement learning Reliability engineering Trajectory Behavioral sciences Recycling Mobile robots Data mining

Compact Multi-Object Placement Using Adjacency-Aware Reinforcement Learning

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arXiv 2024年

作者： Kreis, Benedikt Dengler, Nils de Heuvel, Jorge Menon, Rohit Perur, Hamsa Bennewitz, Maren Humanoid Robots Lab University of Bonn Germany Center for Robotics University of Bonn Germany Lamarr Institute for ML and AI Bonn Germany

关键词： Degrees of freedom (mechanics)