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IEEE International Conference on robotics and Automation (ICRA)

作者： Amir Firouzeh Seyed Sina Mirrazavi Salehian Aude Billard Jamie Paik Amir Firouzeh and Jamie paik are with Reconfigurable Robotics Lab at EPFL Lausanne Ecole Polytechnique Federale de Lausanne Lausanne VD CH Seyed Sina Mirrazavi Salehian and Aude Billard are with Learning Algorithms and Systems Laboratory at EPFL Lausanne

ISBN: (纸本)9781479969241

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.

关键词： Joints Robots Heating Polymers Tendons Shape Temperature

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Recurrent neural state estimation in domains with long-term dependencies 20th

Recurrent neural state estimation in domains with long-term ...

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20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine learning, ESANN 2012

作者： Duell, Siegmund Weichbrodt, Lina Hans, Alexander Udluft, Steffen Siemens AG Corporate Technology Intelligent Systems and Control Otto-Hahn-Ring 6 Munich81739 Germany Berlin University of Technology Machine Learning Franklinstr. 28-29 Berlin10587 Germany Otto-von-Guericke-University Magdeburg P.O.Box 4120 Magdeburg39016 Germany Ilmenau University of Technology Neuroinformatics and Cognitive Robotics Lab P.O.Box 100565 Ilmenau98684 Germany

ISBN: (纸本)9782874190490

This paper presents a state estimation approach for reinforcement learning (RL) of a partially observable Markov decision process. It is based on a special recurrent neural network architecture, the Markov decision process extraction network with shortcuts (MPEN-S). In contrast to previous work regarding this topic, we address the problem of long-term dependencies, which cause major problems in many real-world applications. The architecture is designed to model the reward-relevant dynamics of an environment and is capable to condense large sets of continuous observables to a compact Markovian state representation. The resulting estimate can be used as input for RL methods that assume the underlying system to be a Markov decision process. Although the approach was developed with RL in mind, it is also useful for general prediction tasks. © 2012, *** publication. All rights reserved.

关键词： State estimation

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Deep Reinforcement learning for the Joint Control of Traffic Light Signaling and Vehicle Speed Advice

Deep Reinforcement Learning for the Joint Control of Traffic...

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International Conference on Machine learning and Applications (ICMLA)

作者： Johannes V. S. Busch Robert Voelckner Peter Sossalla Christian L. Vielhaus Roberto Calandra Frank H. P. Fitzek Learning Adaptive Systems and Robotics (LASR) Lab TU Dresden Germany Center for Tactile Internet with Human-in-the-Loon (CeTI)‘ TU Dresden Germany Deutsche Telekom Chair of Communication Networks TU Dresden Germany Audi AG Ingolstadt Germany

Traffic congestion in dense urban centers presents an economical and environmental burden. In recent years, the availability of vehicle- to-anything communication allows for the transmission of detailed vehicle states to the infrastructure that can be used for intelligent traffic light control. The other way around, the infrastructure can provide vehicles with advice on driving behavior, such as appropriate velocities, which can improve the efficacy of the traffic system. Several research works applied deep reinforcement learning to either traffic light control or vehicle speed advice. In this work, we propose a first attempt to jointly learn the control of both. We show this to improve the efficacy of traffic systems. In our experiments, the joint control approach reduces average vehicle trip delays, w.r.t. controlling only traffic lights, in eight out of eleven benchmark scenarios. Analyzing the qualitative behavior of the vehicle speed advice policy, we observe that this is achieved by smoothing out the velocity profile of vehicles nearby a traffic light. learning joint control of traffic signaling and speed advice in the real world could help to reduce congestion and mitigate the economical and environmental repercussions of today's traffic systems.

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Safe Stop Trajectory Planning for Highly Automated Vehicles: An Optimal Control Problem Formulation

Safe Stop Trajectory Planning for Highly Automated Vehicles:...

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IEEE Symposium on Intelligent Vehicle

作者： Lars Svensson Lola Masson Naveen Mohan Erik Ward Anna Pernestål Brenden Lei Feng Martin Törngren Mechatronics and Embedded Control Systems KTH Royal Institute of Technology Stockholm Sweden LAAS-CNRS University of Toulouse Toulouse France Robotics Perception and Learning KTH Royal Institute of Technology Integrated Transport Research Lab KTH Royal Institute of Technology

Highly automated road vehicles need the capability of stopping safely in a situation that disrupts continued normal operation, e.g. due to internal system faults. Motion planning for safe stop differs from nominal motion planning, since there is not a specific goal location. Rather, the desired behavior is that the vehicle should reach a stopped state, preferably outside of active lanes. Also, the functionality to stop safely needs to be of high integrity. The first contribution of this paper is to formulate the safe stop problem as a benchmark optimal control problem, which can be solved by dynamic programming. However, this solution method cannot be used in real-time. The second contribution is to develop a real-time safe stop trajectory planning algorithm, based on selection from a precomputed set of trajectories. By exploiting the particular properties of the safe stop problem, the cardinality of the set is decreased, making the algorithm computationally efficient. Furthermore, a monitoring based architecture concept is proposed, that ensures dependability of the safe stop function. Finally, a proof of concept simulation using the proposed architecture and the safe stop trajectory planner is presented.

关键词： Trajectory Planning Safety Monitoring Computer architecture Optimization Vehicle dynamics

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On the programming effort required to generate Behavior Trees and Finite State Machines for robotic applications

On the programming effort required to generate Behavior Tree...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Matteo Iovino Julian Förster Pietro Falco Jen Jen Chung Roland Siegwart Christian Smith ABB Corporate Research Västerås Sweden Division of Robotics Perception and Learning KTH - Royal Institute of Technology Stockholm Sweden Autonomous Systems Lab ETH Zürich Zürich Switzerland School of ITEE The University of Queensland Australia

In this paper we provide a practical demonstration of how the modularity in a Behavior Tree (BT) decreases the effort in programming a robot task when compared to a Finite State Machine (FSM). In recent years the way to represent a task plan to control an autonomous agent has been shifting from the standard FSM towards BTs. Many works in the literature have highlighted and proven the benefits of such design compared to standard approaches, especially in terms of modularity, reactivity and human readability. However, these works have often failed in providing a tangible comparison in the implementation of those policies and the programming effort required to modify them. This is a relevant aspect in many robotic applications, where the design choice is dictated both by the robustness of the policy and by the time required to program it. In this work, we compare backward chained BTs with a fault-tolerant design of FSMs by evaluating the cost to modify them. We validate the analysis with a set of experiments in a simulation environment where a mobile manipulator solves an item fetching task.

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learning task-specific dynamics to improve whole-body control

arXiv

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arXiv 2018年

作者： Gams, Andrej Mason, Sean A. Ude, Aleš Schaal, Stefan Righetti, Ludovic Humanoid and Cognitive Robotics Lab Dept. of Automatics Bio-cybernetics and Robotics Jožef Stefan Institute Ljubljana Slovenia Computational Learning and Motor Control Lab University of Southern California Los AngelesCA United States Tandon School of Engineering New York University New York United States Max Planck Institute for Intelligent Systems Tuebingen Germany

In task-based inverse dynamics control, reference accelerations used to follow a desired plan can be broken down into feedforward and feedback trajectories. The feedback term accounts for tracking errors that are caused from inaccurate dynamic models or external disturbances. On underactuated, free-floating robots, such as humanoids, good tracking accuracy often necessitates high feedback gains, which leads to undesirable stiff behaviors. The magnitude of these gains is anyways often strongly limited by the control bandwidth. In this paper, we show how to reduce the required contribution of the feedback controller by incorporating learned task-space reference accelerations. Thus, we i) improve the execution of the given specific task, and ii) offer the means to reduce feedback gains, providing for greater compliance of the system. In contrast to learning task-specific joint-torques, which might produce a similar effect but can lead to poor generalization, our approach directly learns the task-space dynamics of the center of mass of a humanoid robot. Simulated and real-world results on the lower part of the Sarcos Hermes humanoid robot demonstrate the applicability of the approach. Copyright © 2018, The Authors. All rights reserved.

关键词： Feedback

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On the programming effort required to generate Behavior Trees and Finite State Machines for robotic applications

arXiv

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arXiv 2022年

作者： Iovino, Matteo Förster, Julian Falco, Pietro Chung, Jen Jen Siegwart, Roland Smith, Christian ABB Corporate Research Västerås Sweden Division of Robotics Perception and Learning KTH - Royal Institute of Technology Stockholm Sweden Autonomous Systems Lab ETH Zürich Zürich Switzerland School of ITEE The University of Queensland Australia

关键词： Autonomous agents

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Evaluation of an Industrial Robotic Assistant in an Ecological Environment

Evaluation of an Industrial Robotic Assistant in an Ecologic...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Baptiste Busch Giuseppe Cotugno Mahdi Khoramshahi Grigorios Skaltsas Dario Turchi Leonardo Urbano Mirko Wächter You Zhou Tamim Asfour Graham Deacon Duncan Russell Aude Billard EPFL Learning Algorithms and Systems Laboratory (LASA) Lausanne Switzerland Robotics Research Group of Ocado Technology Hatfield UK University of Hertfordshire Hatfield UK KIT High Performance Humanoid Technologies Lab (H2T) Karlsruhe Germany

Social robotic assistants have been widely studied and deployed as telepresence tools or caregivers. Evaluating their design and impact on the people interacting with them is of prime importance. In this research, we evaluate the usability and impact of ARMAR-6, an industrial robotic assistant for maintenance tasks. For this evaluation, we have used a modified System Usability Scale (SUS) to assess the general usability of the robotic system and the Godspeed questionnaire series for the subjective perception of the coworker. We have also recorded the subjects' gaze fixation patterns and analyzed how they differ when working with the robot compared to a human partner.

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Where is my keyboard? Model-based active adaptation of action-space in a humanoid robot

Where is my keyboard? Model-based active adaptation of actio...

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IEEE-RAS International Conference on Humanoid Robots

作者： Arturo Ribes Jesus Cerquides Yiannis Demiris Ramon Lopez de Mantaras Learning Systems department at IIIA-CSIC (UAB) IIIA-CSIC (UAB) Personal Robotics Lab at the Imperial College of London

ISBN: (纸本)9781479968862

Nowadays robots are becoming more ubiquitous, and focus is increasingly put on the variety of tasks they can perform autonomously. However, due to the dynamics of the environment or the robot itself, sometimes the models that were learned in the past do not exactly fit in the present situation. Adaptation mechanisms are of key importance in this case since they enable the robot to reuse prior knowledge, which is useful for the current context. In this work we introduce an active adaptation mechanism which enables a humanoid robot to recover from a failure, exemplified as a displacement of the object it is interacting with.

关键词： Humanoid robots Robot Keyboard Activity spaces Prior learning

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Preventing Unconstrained CBF Safety Filters Caused by Invalid Relative Degree Assumptions

arXiv

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arXiv 2024年

作者： Brunke, Lukas Zhou, Siqi Schoellig, Angela P. Learning Systems and Robotics Lab The Munich Institute of Robotics and Machine Intelligence Technical University of Munich Munich80333 Germany University of Toronto Institute for Aerospace Studies North YorkONM3H 5T6 Canada University of Toronto Robotics Institute TorontoONM5S 1A4 Canada Vector Institute for Artificial Intelligence TorontoONM5G 0C6 Canada

Control barrier function (CBF)-based safety filters are used to certify and modify potentially unsafe control inputs to a system such as those provided by a reinforcement learning agent or a non-expert user. In this context, safety is defined as the satisfaction of state constraints. Originally designed for continuous-time systems, CBF safety filters typically assume that the system’s relative degree is well-defined and is constant across the domain;however, this assumption is restrictive and rarely verified—even linear system dynamics with a quadratic CBF candidate may not satisfy this assumption. In real-world applications, continuous-time CBF safety filters are implemented in discrete time, exacerbating issues related to violating the condition on the relative degree. These violations can lead to the safety filter being unconstrained (any control input may be certified) for a finite time interval and result in chattering issues and constraint violations. We propose an alternative formulation to address these challenges. Specifically, we present a theoretically sound method that employs multiple CBFs to generate bounded control inputs at each state within the safe set, thereby preventing incorrect certification of arbitrary control inputs. Using this approach, we derive conditions on the maximum sampling time to ensure safety in discrete-time implementations. We demonstrate the effectiveness of our proposed method through simulations and real-world quadrotor experiments, successfully preventing chattering and constraint violations. Finally, we discuss the implications of violating the relative degree condition on CBF synthesis and learning-based CBF methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Continuous time systems

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