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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

Relative Representations: Topological and Geometric Perspectives

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arXiv 2024年

作者： García-Castellanos, Alejandro Marchetti, Giovanni Luca Kragic, Danica Scolamiero, Martina Amsterdam Machine Learning Lab University of Amsterdam Netherlands Department of Mathematics KTH Royal Institute of Technology Sweden Division of Robotics Perception and Learning KTH Royal Institute of Technology Sweden

Relative representations are an established approach to zero-shot model stitching, consisting of a non-trainable transformation of the latent space of a deep neural network. Based on insights of topological and geometric nature, we propose two improvements to relative representations. First, we introduce a normalization procedure in the relative transformation, resulting in invariance to non-isotropic rescalings and permutations. The latter coincides with the symmetries in parameter space induced by common activation functions. Second, we propose to deploy topological densification when fine-tuning relative representations, a topological regularization loss encouraging clustering within classes. We provide an empirical investigation on a natural language task, where both the proposed variations yield improved performance on zero-shot model stitching. © 2024, CC BY.

关键词： Topology

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

MCDS-VSS: Moving Camera Dynamic Scene Video Semantic Segmentation by Filtering with Self-Supervised Geometry and Motion

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arXiv 2024年

作者： Villar-Corrales, Angel Austermann, Moritz Behnke, Sven Autonomous Intelligent Systems Computer Science Institute VI Center for Robotics Lamarr Institute for Machine Learning and Artificial Intelligence University of Bonn Germany

Autonomous systems, such as self-driving cars, rely on reliable semantic environment perception for decision making. Despite great advances in video semantic segmentation, existing approaches ignore important inductive biases and lack structured and interpretable internal representations. In this work, we propose MCDS-VSS, a structured filter model that learns in a self-supervised manner to estimate scene geometry and ego-motion of the camera, while also estimating the motion of external objects. Our model leverages these representations to improve the temporal consistency of semantic segmentation without sacrificing segmentation accuracy. MCDS-VSS follows a prediction-fusion approach in which scene geometry and camera motion are first used to compensate for ego-motion, then residual flow is used to compensate motion of dynamic objects, and finally the predicted scene features are fused with the current features to obtain a temporally consistent scene segmentation. Our model parses automotive scenes into multiple decoupled interpretable representations such as scene geometry, ego-motion, and object motion. Quantitative evaluation shows that MCDS-VSS achieves superior temporal consistency on video sequences while retaining competitive segmentation performance. Code and pretrained models are available in the project website. © 2024, CC BY.

关键词： Semantic Segmentation

RoboCup@Home 2024 OPL Winner NimbRo: Anthropomorphic Service Robots using Foundation Models for Perception and Planning

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arXiv 2024年

作者： Memmesheimer, Raphael Nogga, Jan Pätzold, Bastian Kruzhkov, Evgenii Bultmann, Simon Schreiber, Michael Bode, Jonas Karacora, Bertan Park, Juhui Savinykh, Alena Behnke, Sven Autonomous Intelligent Systems Computer Science Institute VI Lamarr Institute for Machine Learning and Artificial Intelligence Center for Robotics University of Bonn Germany

We present the approaches and contributions of the winning team NimbRo@Home at the RoboCup@Home 2024 competition in the Open Platform League held in Eindhoven, NL. Further, we describe our hardware setup and give an overview of the results for the task stages and the final demonstration. For this year's competition, we put a special emphasis on open-vocabulary object segmentation and grasping approaches that overcome the labeling overhead of supervised vision approaches, commonly used in RoboCup@Home. We successfully demonstrated that we can segment and grasp non-labeled objects by text descriptions. Further, we extensively employed Large Language Models (LLMs) for natural language understanding and task planning. Throughout the competition, our approaches showed robustness and generalization capabilities. A video of our performance can be found online. Copyright © 2024, The Authors. All rights reserved.

关键词： Anthropomorphic robots

One Policy to Run Them All: an End-to-end learning Approach to Multi-Embodiment Locomotion 8

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One Policy to Run Them All: an End-to-end Learning Approach ...

8th Conference on Robot learning, CoRL 2024

作者： Bohlinger, Nico Czechmanowski, Grzegorz Krupka, Maciej Kicki, Piotr Walas, Krzysztof Peters, Jan Tateo, Davide Department of Computer Science Technical University of Darmstadt Germany Institute of Robotics and Machine Intelligence Poznan University of Technology Poland Research Department: Systems AI for Robot Learning Germany IDEAS NCBR Warsaw Poland Hessian.AI Germany Centre for Cognitive Science Germany

Deep Reinforcement learning techniques are achieving state-of-the-art results in robust legged locomotion. While there exists a wide variety of legged platforms such as quadruped, humanoids, and hexapods, the field is still missing a single learning framework that can control all these different embodiments easily and effectively and possibly transfer, zero or few-shot, to unseen robot embodiments. We introduce URMA, the Unified Robot Morphology Architecture, to close this gap. Our framework brings the end-to-end Multi-Task Reinforcement learning approach to the realm of legged robots, enabling the learned policy to control any type of robot morphology. The key idea of our method is to allow the network to learn an abstract locomotion controller that can be seamlessly shared between embodiments thanks to our morphology-agnostic encoders and decoders. This flexible architecture can be seen as a potential first step in building a foundation model for legged robot locomotion. Our experiments show that URMA can learn a locomotion policy on multiple embodiments that can be easily transferred to unseen robot platforms in simulation and the real world. © 2024 Proceedings of machine learning Research.

关键词： Deep reinforcement learning

Dexterous Pre-grasp Manipulation for Human-like Functional Categorical Grasping: Deep Reinforcement learning and Grasp Representations

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arXiv 2023年

作者： Pavlichenko, Dmytro Behnke, Sven Group Computer Science Institute VI – Intelligent Systems and Robotics The Center for Robotics The Lamarr Institute for Machine Learning and Artificial Intelligence University of Bonn Germany

Many objects, such as tools and household items, can be used only if grasped in a very specific way—grasped functionally. Often, a direct functional grasp is not possible, though. We propose a method for learning a dexterous pre-grasp manipulation policy to achieve human-like functional grasps using deep reinforcement learning. We introduce a dense multi-component reward function that enables learning a single policy, capable of dexterous pre-grasp manipulation of novel instances of several known object categories with an anthropomorphic hand. The policy is learned purely by means of reinforcement learning from scratch, without any expert demonstrations. It implicitly learns to reposition and reorient objects of complex shapes to achieve given functional grasps. In addition, we explore two different ways to represent a desired grasp: explicit and more abstract, constraint-based. We show that our method consistently learns to successfully manipulate and achieve desired grasps on previously unseen object instances of known categories using both grasp representations. Training is completed on a single GPU in under three hours. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep reinforcement learning

Radar Tracker: Moving Instance Tracking in Sparse and Noisy Radar Point Clouds

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Radar Tracker: Moving Instance Tracking in Sparse and Noisy ...

IEEE International Conference on robotics and Automation (ICRA)

作者： Matthias Zeller Daniel Casado Herraez Jens Behley Michael Heidingsfeld Cyrill Stachniss CARIAD SE and Center for Robotics University of Bonn Germany Center for Robotics University of Bonn Germany CARIAD SE Germany Lamarr Institute for Machine Learning and Artificial Intelligence Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Robots and autonomous vehicles should be aware of what happens in their surroundings. The segmentation and tracking of moving objects are essential for reliable path planning, including collision avoidance. We investigate this estimation task for vehicles using radar sensing. We address moving instance tracking in sparse radar point clouds to enhance scene interpretation. We propose a learning-based radar tracker incorporating temporal offset predictions to enable direct center-based association and enhance segmentation performance by including additional motion cues. We implement attention-based tracking for sparse radar scans to include appearance features and enhance performance. The final association combines geometric and appearance features to overcome the limitations of center-based tracking to associate instances reliably. Our approach shows an improved performance on the moving instance tracking benchmark of the RadarScenes dataset compared to the current state of the art.

关键词： Point cloud compression Tracking Radar Benchmark testing Radar tracking Robot sensing systems Reliability

Multi-Step Model Predictive Safety Filters: Reducing Chattering by Increasing the Prediction Horizon

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Multi-Step Model Predictive Safety Filters: Reducing Chatter...

IEEE Conference on Decision and Control

作者： Federico Pizarro Bejarano Lukas Brunke Angela P. Schoellig the Learning Systems and Robotics Lab University of Toronto Robotics Institute and the Vector Institute for Artificial Intelligence Toronto Canada Technical University of Munich and the Munich Institute for Robotics and Machine Intelligence (MIRMI) Germany

learning-based controllers have demonstrated su-perior performance compared to classical controllers in various tasks. However, providing safety guarantees is not trivial. Safety, the satisfaction of state and input constraints, can be guaranteed by augmenting the learned control policy with a safety filter. Model predictive safety filters (MPSFs) are a common safety filtering approach based on model predictive control (MPC). MPSFs seek to guarantee safety while minimizing the difference between the proposed and applied inputs in the immediate next time step. This limited foresight can lead to jerky motions and undesired oscillations close to constraint boundaries, known as chattering. In this paper, we reduce chattering by considering input corrections over a longer horizon. Under the assumption of bounded model uncertainties, we prove recursive feasibility using techniques from robust MPC. We verified the proposed approach in both extensive simulation and quadrotor exper-iments. In experiments with a Crazyflie 2.0 drone, we show that, in addition to preserving the desired safety guarantees, the proposed MPSF reduces chattering by more than a factor of 4 compared to previous MPSF formulations.

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