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

作者： Chen, Nutan Cseke, Botond Aljalbout, Elie Paraschos, Alexandros Alles, Marvin Van Der Smagt, Patrick Machine Learning Research Lab Volkswagen Group Germany Robotics and Perception Group Department of Informatics Switzerland Uzh Eth Zurich Department of Neuroinformatics Switzerland Eötvös Loránd University Faculty of Informatics Budapest Hungary

ISBN: (纸本)9798350373578

We present a novel motion generation approach for robot arms, with high degrees of freedom, in complex settings that can adapt online to obstacles or new via points. learning from Demonstration facilitates rapid adaptation to new tasks and optimizes the utilization of accumulated expertise by allowing robots to learn and generalize from demonstrated trajectories. We train a transformer architecture, based on conditional variational autoencoder, on a large dataset of simulated trajectories used as demonstrations. Our architecture learns essential motion generation skills from these demonstrations and is able to adapt them to meet auxiliary tasks. Additionally, our approach implements auto-regressive motion generation to enable real-time adaptations, as, for example, introducing or changing via-points, and velocity and acceleration constraints. Using beam search, we present a method for further adaption of our motion generator to avoid obstacles. We show that our model successfully generates motion from different initial and target points and that is capable of generating trajectories that navigate complex tasks across different robotic platforms. © 2024 IEEE.

关键词： Trajectories

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SimpleMapping: Real-Time Visual-Inertial Dense Mapping with Deep Multi-View Stereo

SimpleMapping: Real-Time Visual-Inertial Dense Mapping with ...

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International Symposium on Mixed and Augmented Reality (ISMAR)

作者： Yingye Xin Xingxing Zuo Dongyue Lu Stefan Leutenegger Smart Robotics Lab Technical University of Munich Germany Munich Center for Machine Learning (MCML) Germany

We present a real-time visual-inertial dense mapping method capable of performing incremental 3D mesh reconstruction with high quality using only sequential monocular images and inertial measurement unit (IMU) readings. 6-DoF camera poses are estimated by a robust feature-based visual-inertial odometry (VIO), which also generates noisy sparse 3D map points as a by-product. We propose a sparse point aided multi-view stereo neural network (SPA-MVSNet) that can effectively leverage the informative but noisy sparse points from the VIO system. The sparse depth from VIO is firstly completed by a single-view depth completion network. This dense depth map, although naturally limited in accuracy, is then used as a prior to guide our MVS network in the cost volume generation and regularization for accurate dense depth prediction. Predicted depth maps of keyframe images by the MVS network are incrementally fused into a global map using TSDF-Fusion. We extensively evaluate both the proposed SPA-MVSNet and the entire dense mapping system on several public datasets as well as our own dataset, demonstrating the system’s impressive generalization capabilities and its ability to deliver high-quality 3D reconstruction online. Our proposed dense mapping system achieves a 39.7% improvement in F-score over existing systems when evaluated on the challenging scenarios of the EuRoC dataset.

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Caging Complex Objects with Geodesic Balls

Caging Complex Objects with Geodesic Balls

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Dmitry Zarubin Florian T. Pokorny Marc Toussaint Danica Kragic Machine Learning and Robotics Lab Universitat Stuttgart Stuttgart Germany Centre for Autonomous Systems Computer Vision and Active Perception Lab School of Computer Science and Communication KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781467363563

This paper proposes a novel approach for the synthesis of grasps of objects whose geometry can be observed only in the presence of noise. We focus in particular on the problem of generating caging grasps with a realistic robot hand simulation and show that our method can generate such grasps even on complex objects. We introduce the idea of using geodesic balls on the object's surface in order to approximate the maximal contact surface between a robotic hand and an object. We define two types of heuristics which extract information from approximate geodesic balls in order to identify areas on an object that can likely be used to generate a caging grasp. Our heuristics are based on two scoring functions. The first uses winding angles measuring how much a geodesic ball on the surface winds around a dominant axis, while the second explores using the total discrete Gaussian curvature of a geodesic ball to rank potential caging postures. We evaluate our approach with respect to variations in hand kinematics, for a selection of complex real-world objects and with respect to its robustness to noise.

关键词： Caging Complex Objects Geodesic Balls Gaussian curvature

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Planning Ergonomic Sequences of Actions in Human-Robot Interaction

Planning Ergonomic Sequences of Actions in Human-Robot Inter...

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IEEE International Conference on robotics and Automation

作者： Baptiste Busch Marc Toussaint Manuel Lopes FLOWERS Team an Inria BSO ENSTA Paritech joint laboratory France Machine Learning and Robotics Lab University of Stuttgart Germany INESC-ID Universide de Lisboa Portugal

In this paper, we define the problem of human-robot collaboration as a combined task and motion planning problem which is extended to the multi-agent case (human and robot). Our proposed approach allows us to explicitly take into account ergonomic cost, synchrony and concurrency of behavior in an optimization formulation. We show simulated results as well as an experiment with a real robot combined with a user study. Results show that optimizing over a sequence of actions leads to more ergonomic situations.

关键词： Ergonomics Task analysis Robot kinematics Planning Cost function

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Is Data All That Matters? the Role of Control Frequency for learning-Based Sampled-Data Control of Uncertain Systems

Is Data All That Matters? the Role of Control Frequency for ...

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American Control Conference (ACC)

作者： Ralf Römer Lukas Brunke Siqi Zhou Angela P. Schoellig Learning Systems and Robotics Lab (***) School of Computation Information and Technology and the Munich Institute for Robotics and Machine Intelligence (MIRMI) Technical University of Munich Germany

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

learning models or control policies from data has become a powerful tool to improve the performance of uncertain systems. While a strong focus has been placed on increasing the amount and quality of data to improve performance, data can never fully eliminate uncertainty, making feedback necessary to ensure stability and performance. We show that the control frequency at which the input is recalculated is a crucial design parameter, yet it has hardly been considered before. We address this gap by combining probabilistic model learning and sampled-data control. We use Gaussian processes (GPs) to learn a continuous-time model and compute a corresponding discrete-time controller. The result is an uncertain sampled-data control system, for which we derive robust stability conditions. We formulate semidefinite programs to compute the minimum control frequency required for stability and to optimize performance. As a result, our approach enables us to study the effect of both control frequency and data on stability and closed-loop performance. We show in numerical simulations of a quadrotor that performance can be improved by increasing either the amount of data or the control frequency, and that we can trade off one for the other. For example, by increasing the control frequency by 33%, we can reduce the number of data points by half while still achieving similar performance.

关键词： Uncertain systems Uncertainty Robust stability Computational modeling Control systems Stability analysis Data models

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Reinforcement learning with Non-uniform State Representations for Adaptive Search

Reinforcement Learning with Non-uniform State Representation...

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IEEE International Workshop on Safety, Security, and Rescue robotics (SSRR)

作者： Sandeep Manjanna Herke van Hoof Gregory Dudek Mobile Robotics Lab (MRL) McGill University Montreal Canada Amsterdam Machine Learning Lab (AMLAB) University of Amsterdam Amsterdam Netherlands

The following topics are dealt with: mobile robots; path planning; robot vision; autonomous aerial vehicles; remotely operated vehicles; rescue robots; cameras; multi-robot systems; emergency services; learning (artif... 详细信息

ISBN: (纸本)9781538655733

关键词： Robots Probability distribution Search problems learning (artificial intelligence) Trajectory Task analysis

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

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

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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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Practical Considerations for Discrete-Time Implementations of Continuous-Time Control Barrier Function-Based Safety Filters

Practical Considerations for Discrete-Time Implementations o...

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American Control Conference (ACC)

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

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

Safety filters based on control barrier functions (CBFs) have become a popular method to guarantee safety for uncertified control policies, e.g., as resulting from reinforcement learning. Here, safety is defined as staying in a pre-defined set, the safe set, that adheres to the system's state constraints, e.g., as given by lane boundaries for a self-driving vehicle. In this paper, we examine one commonly overlooked problem that arises in practical implementations of continuous-time CBF-based safety filters. In particular, we look at the issues caused by discrete-time implementations of the continuous-time CBF-based safety filter, especially for cases where the magnitude of the Lie derivative of the CBF with respect to the control input is zero or close to zero. When overlooked, this filter can result in undesirable chattering effects or constraint violations. In this work, we propose three mitigation strategies that allow us to use a continuous-time safety filter in a discrete-time implementation with a local relative degree. Using these strategies in augmented CBF-based safety filters, we achieve safety for all states in the safe set by either using an additional penalty term in the safety filtering objective or modifying the CBF such that those undesired states are not encountered during closed-loop operation. We demonstrate the presented issue and validate our three proposed mitigation strategies in simulation and on a real-world quadrotor.

关键词： Filters Prevention and mitigation Transforms Switches Reinforcement learning Control systems Safety

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The Markov decision process extraction network

The Markov decision process extraction network

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18th European Symposium on Artificial Neural Networks, Computational Intelligence and machine learning, ESANN 2010

作者： Duell, Siegmund Hans, Alexander Udluft, Steffen Siemens AG Corporate Research and Technologies Learning Systems Otto-Hahn-Ring 6 D-81739 Munich Germany Berlin University of Technology Machine Learning Franklinstr. 28-29 D-10587 Berlin Germany Ilmenau University of Technology Neuroinformatics and Cognitive Robotics Lab P.O.Box 100565 D-98684 Ilmenau Germany

ISBN: (纸本)2930307102

This paper presents the Markov decision process extraction network, which is a data-efficient, automatic state estimation approach for discrete-time reinforcement learning (RL) based on recurrent neural networks. The architecture is designed to model the minimal relevant dynamics of an environment, capable of condensing large sets of continuous observables to a compact state representation and excluding irrelevant information. To the best of our knowledge, it is the first approach published to automatically extract minimal relevant aspects of the dynamics from observations to model a Markov decision process, suitable for RL, without requiring special knowledge of the regarded environment. The capabilities of the neural state estimation approach are evaluated using the cart-pole problem and standard table-based policy iteration.

关键词： Markov processes

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learning Adaptive Multi-Objective Robot Navigation Incorporating Demonstrations

arXiv

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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

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