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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

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

关键词： Degrees of freedom (mechanics)

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

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arXiv 2023年

作者： Dawood, Murad Pan, Sicong Dengler, Nils Zhou, Siqi Schoellig, Angela P. Bennewitz, Maren The Humanoid Robots Lab University of Bonn Germany The Lamarr Institute for Machine Learning and Artificial Intelligence and the Center for Robotics Bonn Germany The Learning Systems and Robotics lab The Technical University of Munich 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. © 2023, CC0.

关键词： Adversarial machine learning

HortiBot: An Adaptive Multi-Arm System for Robotic Horticulture of Sweet Peppers

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arXiv 2024年

作者： Lenz, Christian Menon, Rohit Schreiber, Michael Jacob, Melvin Paul Behnke, Sven Bennewitz, Maren Autonomous Intelligent Systems Lab University of Bonn Germany Humanoid Robots Lab University of Bonn Germany Center for Robotics Bonn Germany The Lamarr Institute Bonn Germany

Horticultural tasks such as pruning and selective harvesting are labor intensive and horticultural staff are hard to find. Automating these tasks is challenging due to the semi-structured greenhouse workspaces, changing environmental conditions such as lighting, dense plant growth with many occlusions, and the need for gentle manipulation of non-rigid plant organs. In this work, we present the three-armed system HortiBot, with two arms for manipulation and a third arm as an articulated head for active perception using stereo cameras. Its perception system detects not only peppers, but also peduncles and stems in real time, and performs online data association to build a world model of pepper plants. Collision-aware online trajectory generation allows all three arms to safely track their respective targets for observation, grasping, and cutting. We integrated perception and manipulation to perform selective harvesting of peppers and evaluated the system in lab experiments. Using active perception coupled with end-effector force torque sensing for compliant manipulation, HortiBot achieves high success rates in our indoor pepper plant mock-up. Copyright © 2024, The Authors. All rights reserved.

关键词： End effectors

HortiBot: An Adaptive Multi-Arm System for Robotic Horticulture of Sweet Peppers

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HortiBot: An Adaptive Multi-Arm System for Robotic Horticult...

IEEE/RSJ International Conference on Intelligent robots and Systems (IROS)

作者： Christian Lenz Rohit Menon Michael Schreiber Melvin Paul Jacob Sven Behnke Maren Bennewitz Autonomous Intelligent Systems Lab University of Bonn Germany Center for Robotics Bonn Germany Humanoid Robots Lab University of Bonn Germany The Lamarr Institute Bonn Germany

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

关键词： Torque Target tracking Horticulture Active perception Lighting Robot sensing systems Real-time systems Trajectory Sensors Intelligent robots

Compact Multi-Object Placement Using Adjacency-Aware Reinforcement Learning

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

IEEE-RAS International Conference on humanoid robots

作者： Benedikt Kreis Nils Dengler Jorge de Heuvel Rohit Menon Hamsa Perur Maren Bennewitz Humanoid Robots Lab University of Bonn Germany Center for Robotics University of Bonn Germany 3Lamarr Institute for ML and AI Bonn Germany

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

关键词： Learning systems Layout Fingers humanoid robots Reinforcement learning Robot sensing systems End effectors Grippers Collision avoidance Assembly

Physically-Consistent Parameter Identification of robots in Contact

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arXiv 2024年

作者： Khorshidi, Shahram Dawood, Murad Nederkorn, Benno Bennewitz, Maren Khadiv, Majid Humanoid Robots Lab University of Bonn Germany Lamarr Institute for Machine Learning and Artificial Intelligence the Center for Robotics Bonn Germany Roboverse Reply Munich Germany Germany

Accurate inertial parameter identification is crucial for the simulation and control of robots encountering intermittent contacts with the environment. Classically, robots’ inertial parameters are obtained from CAD models that are not precise (and sometimes not available, e.g., Spot from Boston Dynamics), hence requiring identification. To do that, existing methods require access to contact force measurement, a modality not present in modern quadruped and humanoid robots. This paper presents an alternative technique that utilizes joint current/torque measurements —a standard sensing modality in modern robots— to identify inertial parameters without requiring direct contact force measurements. By projecting the whole-body dynamics into the null space of contact constraints, we eliminate the dependency on contact forces and reformulate the identification problem as a linear matrix inequality that can handle physical and geometrical constraints. We compare our proposed method against a common black-box identification method using a deep neural network and show that incorporating physical consistency significantly improves the sample efficiency and generalizability of the model. Finally, we validate our method on the Spot quadruped robot across various locomotion tasks, showcasing its accuracy and generalizability in real-world scenarios over different gaits. Copyright © 2024, The Authors. All rights reserved.

关键词： Multipurpose robots

Safe Leaf Manipulation for Accurate Shape and Pose Estimation of Occluded Fruits

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arXiv 2024年

作者： Yao, Shaoxiong Pan, Sicong Bennewitz, Maren Hauser, Kris University of Illinois at Urbana-Champaign IL United States Humanoid Robots Lab University of Bonn Germany Lamarr Institute for Machine Learning and Artificial Intelligence The Center for Robotics Bonn Germany

Fruit monitoring plays an important role in crop management, and rising global fruit consumption combined with labor shortages necessitates automated monitoring with robots. However, occlusions from plant foliage often hinder accurate shape and pose estimation. Therefore, we propose an active fruit shape and pose estimation method that physically manipulates occluding leaves to reveal hidden fruits. This paper introduces a framework that plans robot actions to maximize visibility and minimize leaf damage. We developed a novel scene-consistent shape completion technique to improve fruit estimation under heavy occlusion and utilize a perception-driven deformation graph model to predict leaf deformation during planning. Experiments on artificial and real sweet pepper plants demonstrate that our method enables robots to safely move leaves aside, exposing fruits for accurate shape and pose estimation, outperforming baseline methods. Project page: https://***/lmap-ssc/. Copyright © 2024, The Authors. All rights reserved.

关键词： Fruits