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6th International Conference on Graphics and Signal Processing, ICGSP 2022

作者： Bayram, Fatih S. Pütz, Florian Weiß, Julian Radtke, Roman Jesser, Alexander Stache, Nicolaj C. Institute for Intelligent Cyber-Physical Systems Heilbronn University of Applied Sciences Germany Center for Machine Learning Heilbronn University of Applied Sciences Germany

ISBN: (纸本)9781450396370

This paper involves the development of an intelligent delineator for road traffic detecting potential conflict situations between motor vehicles and vulnerable road users at an early stage. By emitting warning signals, collisions between the road users concerned can then be prevented. The prototype used here includes, among other sensors, a high-resolution FMCW radar capable of detecting, and imaging objects. The goal of this work is to develop a machine learning (ML) model for object classification of vulnerable road users in radar frames. A 77 GHz chirp-sequence radar is used to record Range-Doppler maps from object classes of car, bicyclist, pedestrian and empty street at different locations. Objective of this is to cover different levels of background noise in the data caused by the different environments due to trees or bushes. For the data acquisition, simple traffic scenarios have been simulated at Heilbronn University. In selecting a suitable ML algorithm for the classifier, the main challenge was that modern machine learning methods are data-based models which require a lot of data and are generally lacking in explainability, such as neural networks. However, the great advantage is that the correlations in the data are learned automatically. With knowledge-based methods, on the other hand, the big advantage is that they are explainable and require much less data, but assume an extensive domain knowledge. Hybrid learning, also called Informed ML, represents a combination of the methods previously mentioned and their advantages. In this paper, one approach from each of these methods is selected as well as trained, and its results are compared to each other. The respective approaches investigated are a deep neural network (DNN), a Support Vector machine (SVM), and a hybrid model of a SVM and a specific neural network for feature extraction called Autoencoder (AE). In this comparison the SVM performs with prediction accuracies around 80%. The hybrid model perfo

关键词： Support vector machines

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PlaySlot: learning Inverse Latent Dynamics for Controllable Object-Centric Video Prediction and Planning

arXiv

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arXiv 2025年

作者： Villar-Corrales, Angel Behnke, Sven Computer Science Institute VI – Intelligent Systems and Robotics Center for Robotics The Lamarr Institute for Machine Learning and Artificial Intelligence Germany

Predicting future scene representations is a crucial task for enabling robots to understand and interact with the environment. However, most existing methods rely on video sequences and simulations with precise action annotations, limiting their ability to leverage the large amount of available unlabeled video data. To address this challenge, we propose PlaySlot, an object-centric video prediction model that infers object representations and latent actions from unlabeled video sequences. It then uses these representations to forecast future object states and video frames. PlaySlot allows to generate multiple possible futures conditioned on latent actions, which can be inferred from video dynamics, provided by a user, or generated by a learned action policy, thus enabling versatile and interpretable world modeling. Our results show that PlaySlot outperforms both stochastic and object-centric baselines for video prediction across different environments. Furthermore, we show that our inferred latent actions can be used to learn robot behaviors sample-efficiently from unlabeled video demonstrations. Videos and code are available at https://***/PlaySlot/. © 2025, CC BY.

关键词： Stochastic systems

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LEAP-VO: Long-term Effective Any Point Tracking for Visual Odometry

arXiv

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arXiv 2024年

作者： Chen, Weirong Chen, Le Wang, Rui Pollefeys, Marc TU Munich Germany Munich Center for Machine Learning Germany MPI for Intelligent Systems Germany Microsoft United States

Visual odometry estimates the motion of a moving camera based on visual input. Existing methods, mostly focusing on two-view point tracking, often ignore the rich temporal context in the image sequence, thereby overlooking the global motion patterns and providing no assessment of the full trajectory reliability. These shortcomings hinder performance in scenarios with occlusion, dynamic objects, and low-texture areas. To address these challenges, we present the Long-term Effective Any Point Tracking (LEAP) module. LEAP innovatively combines visual, inter-track, and temporal cues with mindfully selected anchors for dynamic track estimation. Moreover, LEAP’s temporal probabilistic formulation integrates distribution updates into a learnable iterative refinement module to reason about point-wise uncertainty. Based on these traits, we develop LEAP-VO, a robust visual odometry system adept at handling occlusions and dynamic scenes. Our mindful integration showcases a novel practice by employing long-term point tracking as the front-end. Extensive experiments demonstrate that the proposed pipeline significantly outperforms existing baselines across various visual odometry benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Iterative methods

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A Comparison of Prompt Engineering Techniques for Task Planning and Execution in Service Robotics

arXiv

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arXiv 2024年

作者： Bode, Jonas Pätzold, Bastian Memmesheimer, Raphael Behnke, Sven The Autonomous Intelligent Systems group Computer Science Institute VI – Intelligent Systems and Robotics Lamarr Institute for Machine Learning and Artificial Intelligence Center for Robotics University of Bonn Germany

Recent advances in Large Language Models (LLMs) have been instrumental in autonomous robot control and human-robot interaction by leveraging their vast general knowledge and capabilities to understand and reason across a wide range of tasks and scenarios. Previous works have investigated various prompt engineering techniques for improving the performance of LLMs to accomplish tasks, while others have proposed methods that utilize LLMs to plan and execute tasks based on the available functionalities of a given robot platform. In this work, we consider both lines of research by comparing prompt engineering techniques and combinations thereof within the application of high-level task planning and execution in service robotics. We define a diverse set of tasks and a simple set of functionalities in simulation, and measure task completion accuracy and execution time for several state-of-the-art models. We make our code, including all prompts, available at https://***/AIS-Bonn/Prompt_Engineering. Copyright © 2024, The Authors. All rights reserved.

关键词： Human robot interaction

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SLCF-Net: Sequential LiDAR-Camera Fusion for Semantic Scene Completion using a 3D Recurrent U-Net

arXiv

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arXiv 2024年

作者： Cao, Helin Behnke, Sven Autonomous Intelligent Systems group Computer Science Institute VI-Intelligent Systems and Robotics Center for Robotics and the Lamarr Institute for Machine Learning and Artificial Intelligence University of Bonn Germany

We introduce SLCF-Net, a novel approach for the Semantic Scene Completion (SSC) task that sequentially fuses LiDAR and camera data. It jointly estimates missing geometry and semantics in a scene from sequences of RGB images and sparse LiDAR measurements. The images are semantically segmented by a pre-trained 2D U-Net and a dense depth prior is estimated from a depth-conditioned pipeline fueled by Depth Anything. To associate the 2D image features with the 3D scene volume, we introduce Gaussian-decay Depth-prior Projection (GDP). This module projects the 2D features into the 3D volume along the line of sight with a Gaussian-decay function, centered around the depth prior. Volumetric semantics is computed by a 3D U-Net. We propagate the hidden 3D U-Net state using the sensor motion and design a novel loss to ensure temporal consistency. We evaluate our approach on the SemanticKITTI dataset and compare it with leading SSC approaches. The SLCF-Net excels in all SSC metrics and shows great temporal consistency. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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A Comparison of Prompt Engineering Techniques for Task Planning and Execution in Service Robotics

A Comparison of Prompt Engineering Techniques for Task Plann...

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IEEE-RAS International Conference on Humanoid Robots

作者： Jonas Bode Bastian Pätzold Raphael Memmesheimer Sven Behnke Autonomous Intelligent Systems group Computer Science Institute VI – Intelligent Systems and Robotics Lamarr Institute for Machine Learning and Artificial Intelligence and Center for Robotics University of Bonn Germany

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

关键词： Knowledge engineering Service robots Large language models Instruments Humanoid robots Reliability engineering Time measurement Planning Prompt engineering Tuning

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DiffSSC: Semantic LiDAR Scan Completion using Denoising Diffusion Probabilistic Models

arXiv

引用

arXiv 2024年

作者： Cao, Helin Behnke, Sven The Autonomous Intelligent Systems 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

Perception systems play a crucial role in autonomous driving, incorporating multiple sensors and corresponding computer vision algorithms. 3D LiDAR sensors are widely used to capture sparse point clouds of the vehicle’s surroundings. However, such systems struggle to perceive occluded areas and gaps in the scene due to the sparsity of these point clouds and their lack of semantics. To address these challenges, Semantic Scene Completion (SSC) jointly predicts unobserved geometry and semantics in the scene given raw LiDAR measurements, aiming for a more complete scene representation. Building on promising results of diffusion models in image generation and super-resolution tasks, we propose their extension to SSC by implementing the noising and denoising diffusion processes in the point and semantic spaces individually. To control the generation, we employ semantic LiDAR point clouds as conditional input and design local and global regularization losses to stabilize the denoising process. We evaluate our approach on autonomous driving datasets and our approach outperforms the state-of-the-art for SSC. © 2024, CC BY.

关键词： Semantics

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SLCF-Net: Sequential LiDAR-Camera Fusion for Semantic Scene Completion using a 3D Recurrent U-Net

SLCF-Net: Sequential LiDAR-Camera Fusion for Semantic Scene ...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Helin Cao Sven Behnke Autonomous Intelligent Systems Group Computer Science Institute VI – Intelligent Systems and Robotics – and the Center for Robotics and the Lamarr Institute for Machine Learning and Artificial Intelligence University of Bonn Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

关键词： Measurement Image segmentation Three-dimensional displays Laser radar Semantics Pipelines Robot sensing systems

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Grasp Anything: Combining Teacher-Augmented Policy Gradient learning with Instance Segmentation to Grasp Arbitrary Objects

arXiv

引用

arXiv 2024年

作者： Mosbach, Malte Behnke, Sven The Autonomous Intelligent Systems 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

Interactive grasping from clutter, akin to human dexterity, is one of the longest-standing problems in robot learning. Challenges stem from the intricacies of visual perception, the demand for precise motor skills, and the complex interplay between the two. In this work, we present Teacher-Augmented Policy Gradient (TAPG), a novel two-stage learning framework that synergizes reinforcement learning and policy distillation. After training a teacher policy to master the motor control based on object pose information, TAPG facilitates guided, yet adaptive, learning of a sensorimotor policy, based on object segmentation. We zero-shot transfer from simulation to a real robot by using Segment Anything Model for promptable object segmentation. Our trained policies adeptly grasp a wide variety of objects from cluttered scenarios in simulation and the real world based on human-understandable prompts. Furthermore, we show robust zero-shot transfer to novel objects. Videos of our experiments are available at https://***/grasp_anything. Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

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Self-centering 3-DoF Feet Controller for Hands-Free Locomotion Control in Telepresence and Virtual Reality

arXiv

引用

arXiv 2024年

作者： Memmesheimer, Raphael Lenz, Christian Schwarz, Max Schreiber, Michael Behnke, Sven The Autonomous Intelligent Systems group Computer Science Institute VI - Intelligent Systems and Robotics Lamarr Institute for Machine Learning and Artificial Intelligence Center for Robotics University of Bonn Germany

We present a novel seated feet controller for handling 3 Degree of Freedom (DoF) aimed to control locomotion for telepresence robotics and virtual reality environments. Tilting the feet on two axes yields in forward, backward and sideways motion. In addition, a separate rotary joint allows for rotation around the vertical axis. Attached springs on all joints self-center the controller. The HTC Vive tracker is used to translate the trackers' orientation into locomotion commands. The proposed self-centering feet controller was used successfully for the ANA Avatar XPRIZE competition, where a naive operator traversed the robot through a longer distance, surpassing obstacles while solving various interaction and manipulation tasks in between. We publicly provide the models of the mostly 3D-printed feet controller for reproduction. Copyright © 2024, The Authors. All rights reserved.

关键词： Biped locomotion

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