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

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 Bonn53113 Germany Lamarr Institute for Machine Learning and Artificial Intelligence The Center for Robotics Bonn53113 Germany Technical University of Munich Learning Systems and Robotics Lab Munchen80333 Germany

In this letter, we address the problem of behavior-based cooperative navigation of mobile robots usingsafe 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. © 2025 IEEE.

关键词： Adversarial machine learning

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learning More With Less: Sample Efficient Dynamics learning and Model-Based RL for Loco-Manipulation

arXiv

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arXiv 2025年

作者： Hoffman, Benjamin Cheng, Jin Li, Chenhao Coros, Stelian ETH Zurich Switzerland Computational Robotics Lab The Learning and Adaptive Systems Group Robotic Systems Lab ETH Zurich Switzerland

Combining the agility of legged locomotion with the capabilities of manipulation, loco-manipulation platforms have the potential to perform complex tasks in real-world applications. To this end, state-of-the-art quadrupeds with attached manipulators, such as the Boston Dynamics Spot, have emerged to provide a capable and robust platform. However, both the complexity of loco-manipulation control, as well as the black-box nature of commercial platforms pose challenges for developing accurate dynamics models and control policies. We address these challenges by developing a hand-crafted kinematic model for a quadruped-with-arm platform and, together with recent advances in Bayesian Neural Network (BNN)–based dynamics learning using physical priors, efficiently learn an accurate dynamics model from data. We then derive control policies for loco-manipulation via model-based reinforcement learning (RL). We demonstrate the effectiveness of this approach on hardware using the Boston Dynamics Spot with a manipulator, accurately performing dynamic end-effector trajectory tracking even in low data regimes. Copyright © 2025, The Authors. All rights reserved.

关键词： Manipulators

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FlowMP: learning Motion Fields for Robot Planning with Conditional Flow Matching

arXiv

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arXiv 2025年

作者： Nguyen, Khang Le, An T. Pham, Tien Huber, Manfred Peters, Jan Vu, Minh Nhat Learning and Adaptive Robotics Lab University of Texas Arlington United States Intelligent Autonomous Systems Lab TU Darmstadt Germany Cognitive Robotics Lab University of Manchester United Kingdom SAIROL Darmstadt Germany Hessian.AI Darmstadt Germany TU Wien Vienna Austria GmbH Vienna Austria

Prior flow matching methods in robotics have primarily learned velocity fields to morph one distribution of trajectories into another. In this work, we extend flow matching to capture second-order trajectory dynamics, incorporating acceleration effects either explicitly in the model or implicitly through the learning objective. Unlike diffusion models, which rely on a noisy forward process and iterative denoising steps, flow matching trains a continuous transformation (flow) that directly maps a simple prior distribution to the target trajectory distribution without any denoising procedure. By modeling trajectories with second-order dynamics, our approach ensures that generated robot motions are smooth and physically executable, avoiding the jerky or dynamically infeasible trajectories that first-order models might produce. We empirically demonstrate that this second-order conditional flow matching yields superior performance on motion planning benchmarks, achieving smoother trajectories and higher success rates than baseline planners. These findings highlight the advantage of learning acceleration-aware motion fields, as our method outperforms existing motion planning methods in terms of trajectory quality and planning success. Copyright © 2025, The Authors. All rights reserved.

关键词： Trajectories

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FrontierNet: learning Visual Cues to Explore

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IEEE robotics and Automation Letters 2025年第7期10卷 6576-6583页

作者： Sun, Boyang Chen, Hanzhi Leutenegger, Stefan Cadena, Cesar Pollefeys, Marc Blum, Hermann ETH Zurich Computer Vision and Geometry Group Zurich 8092 Switzerland Technical University of Munich Mobile Robotics Lab München 80333 Germany ETH Zurich Mobile Robotics Lab Zurich 8092 Switzerland ETH Zurich Robotic Systems Lab Zurich 8092 Switzerland AI Lab Microsoft Mixed Reality Zurich 8038 Switzerland University of Bonn Lamarr Institute for ML and AI Robot Perception and Learning Lab Bonn 53115 Germany

Exploration of unknown environments is crucial for autonomous robots;it allows them to actively reason and decide on what new data to acquire for different tasks, such as mapping, object discovery, and environmental assessment. Existing solutions, such as frontier-based exploration approaches, rely heavily on 3D map operations, which are limited by map quality and, more critically, often overlook valuable context from visual cues. This work aims at leveraging 2D visual cues for efficient autonomous exploration, addressing the limitations of extracting goal poses from a 3D map. We propose a visual-only frontier-based exploration system, with FrontierNet as its core component. FrontierNet is a learning-based model that (i) proposes frontiers, and (ii) predicts their information gain, from posed RGB images enhanced by monocular depth priors. Our approach provides an alternative to existing 3D-dependent goal-extraction approaches, achieving a 15% improvement in early-stage exploration efficiency, as validated through extensive simulations and real-world experiments. © 2016 IEEE.

关键词： Deep learning Motion and Path Planning Perception and Autonomy

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

arXiv

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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

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Sensor Query Schedule and Sensor Noise Covariances for Accuracy-constrained Trajectory Estimation

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IEEE robotics and Automation Letters 2025年第7期10卷 6983-6990页

作者： Goudar, Abhishek Schoellig, Angela P. Technical University of Munich Learning Systems and Robotics Lab Germany University of Toronto Institute for Aerospace Studies Canada University of Toronto Robotics Institute Munich Institute of Robotics and Machine Intelligence (MIRMI) Vector Institute for Artificial Intelligence Canada

Trajectory estimation involves determining the trajectory of a mobile robot by combining prior knowledge about its dynamic model with noisy observations of its state obtained using sensors. The accuracy of such a procedure is dictated by the system model fidelity and the sensor parameters, such as the accuracy of the sensor (as represented by its noise covariance) and the rate at which it can generate observations, referred to as the sensor query schedule. Intuitively, high-rate measurements from accurate sensors lead to accurate trajectory estimation. However, cost and resource constraints limit the sensor accuracy and its measurement rate. Our work's novel contribution is the estimation of sensor schedules and sensor covariances necessary to achieve a specific estimation accuracy. Concretely, we focus on estimating: (i) the rate or schedule with which a sensor of known covariance must generate measurements to achieve specific estimation accuracy, and alternatively, (ii) the sensor covariance necessary to achieve specific estimation accuracy for a given sensor update rate. We formulate the problem of estimating these sensor parameters as semidefinite programs, which can be solved by off-the-shelf solvers. We validate our approach in simulation and real experiments by showing that the sensor schedules and the sensor covariances calculated using our proposed method achieve the desired trajectory estimation accuracy. Our method also identifies scenarios where certain estimation accuracy is unachievable with the given system and sensor characteristics. © 2016 IEEE.

关键词： Localization Optimization and Optimal control Probability and Statistical Methods

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Self-Supervised learning of Scene-Graph Representations for Robotic Sequential Manipulation Planning 4

Self-Supervised Learning of Scene-Graph Representations for ...

引用

4th Conference on Robot learning, CoRL 2020

作者： Nguyen, Son-Tung Oguz, Ozgur S. Hartmann, Valentin N. Toussaint, Marc Machine Learning & Robotics Lab University of Stuttgart Germany Max Planck Institute for Intelligent Systems Germany Learning and Intelligent Systems Group TU Berlin Germany

We present a self-supervised representation learning approach for visual reasoning and integrate it into a nonlinear program formulation for motion optimization to tackle sequential manipulation tasks. Such problems have usually been addressed by combined task and motion planning approaches, for which spatial relations and logical rules that rely on symbolic representations have to be predefined by the user. We propose to learn relational structures by leveraging visual perception to alleviate the resulting knowledge acquisition bottleneck. In particular, we learn constructing scene-graphs, that represent objects ("red box"), and their spatial relationships ("yellow cylinder on red box"). This representation allows us to plan high-level discrete decisions effectively using graph search algorithms. We integrate the visual reasoning module with a nonlinear optimization method for robot motion planning and verify its feasibility on the classic blocks-world domain. Our proposed framework successfully finds the sequence of actions and enables the robot to execute feasible motion plans to realize the given tasks. © 2020 Proceedings of Machine learning Research. All rights reserved.

关键词： Motion planning

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An under actuated robotic arm with adjustable stiffness shape memory polymer joints

An under actuated robotic arm with adjustable stiffness shap...

引用

2015 IEEE International Conference on robotics and Automation, ICRA 2015

作者： Firouzeh, Amir Mirrazavi Salehian, Seyed Sina Billard, Aude Paik, Jamie Reconfigurable Robotics Lab EPFL Lausanne Switzerland Learning Algorithms and Systems Laboratory EPFL Lausanne Switzerland

ISBN: (纸本)9781479969234

Various robotic applications including surgical instruments, wearable robots and autonomous mobile robots are often constrained with strict design requirements on high degrees of freedom (DoF) and minimal volume and weight. An intuitive design to meet these contradictory requirements is to embed locking mechanism in under actuated robotic manipulators to direct the actuation from a single and remote source to drive different joints on demand. Mechanical clutches do serve such purposes but often are bulky and require auxiliary mechanism making it difficult to justify the high cost adding the additional DoF, especially in cm scale. Here, we introduce an under-actuated robotic arm with shape memory polymer (SMP) joints. Through controlling the temperature, the stiffness of the joints can be adjusted and selected joints will be activated while the rest are fixed in their position. The presented prototype can control the joints independently with a coupled actuation from two stepper motors. Since we have redundant DoFs in the arm, there can be more than one configuration to reach a given position. We use a probabilistic technique to determine the optimum configuration with the minimum number of active joints that can yield the desired posture. In this paper, we report on the performance of the proposed design for the hardware and the configuration planner. © 2015 IEEE.

关键词： Robotic arms

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PLAN-BASED RELAXED REWARD SHAPING FOR GOAL-DIRECTED TASKS 9

PLAN-BASED RELAXED REWARD SHAPING FOR GOAL-DIRECTED TASKS

引用

9th International Conference on learning Representations, ICLR 2021

作者： Schubert, Ingmar Oguz, Ozgur S. Toussaint, Marc Learning and Intelligent Systems Group TU Berlin Germany Max Planck Institute for Intelligent Systems Stuttgart Germany Machine Learning and Robotics Lab University of Stuttgart Germany

In high-dimensional state spaces, the usefulness of Reinforcement learning (RL) is limited by the problem of exploration. This issue has been addressed using potential-based reward shaping (PB-RS) previously. In the present work, we introduce Final-Volume-Preserving Reward Shaping (FV-RS). FV-RS relaxes the strict optimality guarantees of PB-RS to a guarantee of preserved long-term behavior. Being less restrictive, FV-RS allows for reward shaping functions that are even better suited for improving the sample efficiency of RL algorithms. In particular, we consider settings in which the agent has access to an approximate plan. Here, we use examples of simulated robotic manipulation tasks to demonstrate that plan-based FV-RS can indeed significantly improve the sample efficiency of RL over plan-based PB-RS. © 2021 ICLR 2021 - 9th International Conference on learning Representations. All rights reserved.

关键词： Reinforcement learning

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Leveraging Pretrained Latent Representations for Few-Shot Imitation learning on an Anthropomorphic Robotic Hand 23

Leveraging Pretrained Latent Representations for Few-Shot Im...

引用

23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024

作者： Liconti, Davide Toshimitsu, Yasunori Katzschmann, Robert Eth Zurich Soft Robotics Lab Iris D-MAVT Switzerland Max Plank Eth Center for Learning Systems Germany

ISBN: (纸本)9798350373578

In the context of imitation learning applied to anthropomorphic robotic hands, the high complexity of the systems makes learning complex manipulation tasks challenging. However, the numerous datasets depicting human hands in various different tasks could provide us with better knowledge regarding human hand motion. We propose a method to leverage multiple large-scale task-agnostic datasets to obtain latent representations that effectively encode motion subtrajectories that we included in a transformer-based behavior cloning method. Our results demonstrate that employing latent representations yields enhanced performance compared to conventional behavior cloning methods, particularly regarding resilience to errors and noise in perception and proprioception. Furthermore, the proposed approach solely relies on human demonstrations, eliminating the need for teleoperation and, therefore, accelerating the data acquisition process. Accurate inverse kinematics for fingertip retargeting ensures precise transfer from human hand data to the robot, facilitating effective learning and deployment of manipulation policies. Finally, the trained policies have been successfully transferred to a real-world 23Dof robotic system. © 2024 IEEE.

关键词： Inverse kinematics

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