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IEEE International Conference on Robotics and Automation (ICRA)

作者： Victor Dhédin Haolong Li Shahram Khorshidi Lukas Mack Adithya Kumar Chinnakkonda Ravi Avadesh Meduri Paarth Shah Felix Grimminger Ludovic Righetti Majid Khadiv Joerg Stueckler Embodied Vision Group Max Planck Institute for Intelligent Systems Tuebingen Germany Movement Generation and Control Group Max Planck Institute for Intelligent Systems Tuebingen Germany Tandon School of Engineering New York University New York USA Oxford Robotics Institute University of Oxford England Autonomous Motion Department Max Planck Institute for Intelligent Systems Tuebingen Germany

Implementing dynamic locomotion behaviors on legged robots requires a high-quality state estimation module. Especially when the motion includes flight phases, state-of-the-art approaches fail to produce reliable estimation of the robot posture, in particular base height. In this paper, we propose a novel approach for combining visual-inertial odometry (VIO) with leg odometry in an extended Kalman filter (EKF) based state estimator. The VIO module uses a stereo camera and IMU to yield low-drift 3D position and yaw orientation and drift-free pitch and roll orientation of the robot base link in the inertial frame. However, these values have a considerable amount of latency due to image processing and optimization, while the rate of update is quite low which is not suitable for low-level control. To reduce the latency, we predict the VIO state estimate at the rate of the IMU measurements of the VIO sensor. The EKF module uses the base pose and linear velocity predicted by VIO, fuses them further with a second high-rate IMU and leg odometry measurements, and produces robot state estimates with a high frequency and small latency suitable for control. We integrate this lightweight estimation framework with a nonlinear model predictive controller and show successful implementation of a set of agile locomotion behaviors, including trotting and jumping at varying horizontal speeds, on a torque-controlled quadruped robot.

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Model-free Reinforcement Learning for Robust Locomotion using Demonstrations from Trajectory Optimization

arXiv

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arXiv 2021年

作者： Bogdanovic, Miroslav Khadiv, Majid Righetti, Ludovic Movement Generation and Control Group Max Planck Institute for Intelligent Systems Tübingen Germany Machines in Motion Laboratory Tandon School of Engineering New York University New YorkNY United States

We present a general, two-stage reinforcement learning approach to create robust policies that can be deployed on real robots without any additional training using a single demonstration generated by trajectory optimization. The demonstration is used in the first stage as a starting point to facilitate initial exploration. In the second stage, the relevant task reward is optimized directly and a policy robust to environment uncertainties is computed. We demonstrate and examine in detail the performance and robustness of our approach on highly dynamic hopping and bounding tasks on a quadruped robot. Accompanying videos can be viewed here. Copyright © 2021, The Authors. All rights reserved.

关键词： Aerodynamics

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Visual-Inertial and Leg Odometry Fusion for Dynamic Locomotion

arXiv

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arXiv 2022年

作者： Dhédin, Victor Li, Haolong Khorshidi, Shahram Mack, Lukas Ravi, Adithya Kumar Chinnakkonda Meduri, Avadesh Shah, Paarth Grimminger, Felix Righetti, Ludovic Khadiv, Majid Stueckler, Joerg Embodied Vision Group Max Planck Institute for Intelligent Systems Tuebingen Germany Movement Generation and Control Group Max Planck Institute for Intelligent Systems Tuebingen Germany Tandon School of Engineering New York University New York United States Oxford Robotics Institute University of Oxford United Kingdom Autonomous Motion Department Max Planck Institute for Intelligent Systems Tuebingen Germany

关键词： Extended Kalman filters

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Walking control Based on Step Timing Adaptation

Walking Control Based on Step Timing Adaptation

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作者： Khadiv, Majid Herzog, Alexander Moosavian, S. Ali. A. Righetti, Ludovic Righetti, Ludovic Movement Generation and Control Group Max-Planck Institute for Intelligent Systems Tuebingen72076 Germany Google X Mountain ViewCA94043 United States Department of Mechanical Engineering K. N. Toosi University of Technology Tehran*** Iran Department of Electrical and Computer Engineering New York University BrooklynNY11201 United States

Step adjustment can improve the gait robustness of biped robots;however, the adaptation of step timing is often neglected as it gives rise to nonconvex problems when optimized over several footsteps. In this article, we argue that it is not necessary to optimize walking over several steps to ensure gait viability and show that it is sufficient to merely select the next step timing and location. Using this insight, we propose a novel walking pattern generator that optimally selects step location and timing at every control cycle. Our approach is computationally simple compared to standard approaches in the literature, yet guarantees that any viable state will remain viable in the future. We propose a swing foot adaptation strategy and integrate the pattern generator with an inverse dynamics controller that does not explicitly control the center of mass nor the foot center of pressure. This is particularly useful for biped robots with limited control authority over their foot center of pressure, such as robots with point feet or passive ankles. Extensive simulations on a humanoid robot with passive ankles demonstrate the capabilities of the approach in various walking situations, including external pushes and foot slippage, and emphasize the importance of step timing adaptation to stabilize walking. © 2004-2012 IEEE.

关键词： Anthropomorphic robots

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Learning to Explore in Motion and Interaction Tasks

Learning to Explore in Motion and Interaction Tasks

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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Miroslav Bogdanovic Ludovic Righetti Movement Generation and Control group Max-Planck Institute for Intelligent Systems Tübingen Germany

Model free reinforcement learning suffers from the high sampling complexity inherent to robotic manipulation or locomotion tasks. Most successful approaches typically use random sampling strategies which leads to slow policy convergence. In this paper we present a novel approach for efficient exploration that leverages previously learned tasks. We exploit the fact that the same system is used across many tasks and build a generative model for exploration based on data from previously solved tasks to improve learning new tasks. The approach also enables continuous learning of improved exploration strategies as novel tasks are learned. Extensive simulations on a robot manipulator performing a variety of motion and contact interaction tasks demonstrate the capabilities of the approach. In particular, our experiments suggest that the exploration strategy can more than double learning speed, especially when rewards are sparse. Moreover, the algorithm is robust to task variations and parameter tuning, making it beneficial for complex robotic problems.

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Learning variable impedance control for contact sensitive tasks

arXiv

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arXiv 2019年

作者： Bogdanovic, Miroslav Khadiv, Majid Righetti, Ludovic Movement Generation and Control Group Max-Planck Institute for Intelligent Systems Tübingen Germany Tandon School of Engineering New York University United States

Reinforcement learning algorithms have shown great success in solving different problems ranging from playing video games to robotics. However, they struggle to solve delicate robotic problems, especially those involving contact interactions. Though in principle a policy outputting joint torques should be able to learn these tasks, in practice we see that they have difficulty to robustly solve the problem without any structure in the action space. In this paper, we investigate how the choice of action space can give robust performance in presence of contact uncertainties. We propose to learn a policy that outputs impedance and desired position in joint space as a function of system states without imposing any other structure to the problem. We compare the performance of this approach to torque and position control policies under different contact uncertainties. Extensive simulation results on two different systems, a hopper (floating-base) with intermittent contacts and a manipulator (fixed-base) wiping a table, show that our proposed approach outperforms policies outputting torque or position in terms of both learning rate and robustness to environment uncertainty. Copyright © 2019, The Authors. All rights reserved.

关键词： Reinforcement learning

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Learning to explore in motion and interaction tasks

arXiv

引用

arXiv 2019年

作者： Bogdanovic, Miroslav Righetti, Ludovic Movement Generation and Control group Max-Planck Institute for Intelligent Systems Tübingen Germany Tandon School of Engineering New York University United States

关键词： Manipulators

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A whole-body model predictive control scheme including external contact forces and com height variations

arXiv

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arXiv 2018年

作者： Mirjalili, Reihaneh Yousefi-koma, Aghil Shirazi, Farzad A. Nikkhah, Arman Nazemi, Fatemeh Khadiv, Majid School of Mechanical Engineering College of Engineering University of Tehran Tehran Iran School of Mechanical Engineering College of Engineering University of Tehran Tehran Iran Movement Generation and Control group Max-Planck Institute for Intelligent Systems Tuebingen Germany

In this paper, we present an approach for generating a variety of whole-body motions for a humanoid robot. We extend the available Model Predictive control (MPC) approaches for walking on flat terrain to plan for both vertical motion of the Center of Mass (CoM) and external contact forces consistent with a given task. The optimization problem is comprised of three stages, i. e. the CoM vertical motion, joint angles and contact forces planning. The choice of external contact (e. g. hand contact with the object or environment) among all available locations and the appropriate time to reach and maintain a contact are all computed automatically within the algorithm. The presented algorithm benefits from the simplicity of the Linear Inverted Pendulum Model (LIPM), while it overcomes the common limitations of this model and enables us to generate a variety of whole body motions through external contacts. Simulation and experimental implementation of several whole body actions in multi-contact scenarios on a humanoid robot show the capability of the proposed algorithm. Copyright © 2018, The Authors. All rights reserved.

关键词： Anthropomorphic robots

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Walking control based on step timing adaptation

arXiv

引用

arXiv 2017年

作者： Khadiv, Majid Herzogr, Alexander Righetti, Ludovic Righetti, Ludovic Movement Generation and Control Group Max-Planck Institute for Intelligent Systems Germany Google X Department of Mechanical Engineering K. N. Toosi University of Technology Tehran Iran Department of Electrical and Computer Engineering Department of Mechanical and Aerospace Engineering New York University United States

Step adjustment for biped robots has been shown to improve gait robustness, however the adaptation of step timing is often neglected in control strategies because it gives rise to non-convex problems when optimized over several steps. In this paper, we argue that it is not necessary to optimize walking over several steps to guarantee stability and that it is sufficient to merely select the next step timing and location. From this insight, we propose a novel walking pattern generator with linear constraints that optimally selects step location and timing at every control cycle. The resulting controller is computationally simple, yet guarantees that any viable state will remain viable in the future. We propose a swing foot adaptation strategy and show how the approach can be used with an inverse dynamics controller without any explicit control of the center of mass or the foot center of pressure. This is particularly useful for biped robots with limited control authority on their foot center of pressure, such as robots with point feet and robots with passive ankles. Extensive simulations on a humanoid robot with passive ankles subject to external pushes and foot slippage demonstrate the capabilities of the approach in cases where the foot center of pressure cannot be controlled and emphasize the importance of step timing adaptation to stabilize walking. Copyright © 2017, The Authors. All rights reserved.

关键词： Timing circuits

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Rigid vs compliant contact: An experimental study on biped walking

arXiv

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arXiv 2017年

作者： Khadiv, Majid Moosavian, S. Ali A. Yousefi-Koma, Aghil Sadede, Majid Ehsani-Seresht, Abbas Mansouri, Saeed Movement Generation and Control Group Max Planck Institute for Intelligent Systems Tuebingen Department of Mechanical Engineering K. N. Toosi University of Technology Tehran Iran School of Mechanical Engineering College of Engineering University of Tehran Tehran Iran Department of Mechanical Engineering Tarbiat Modares University Tehran Iran Department of Mechanical Engineering Hakim Sabzevari University Sabzevar Department of Mechanical Engineering Sharif University of Technology Tehran Iran

Contact modeling plays a central role in motion planning, simulation and control of legged robots, as legged locomotion is realized through contact. The two prevailing approaches to model the contact consider rigid and compliant premise at interaction ports. Contrary to the dynamics model of legged systems with rigid contact (without impact) which is straightforward to develop, there is no consensus among researchers to employ a standard compliant contact model. Our main goal in this paper is to study the dynamics model structure of bipedal walking systems with rigid contact and a novel compliant contact model, and to present experimental validation of both models. For the model with rigid contact, after developing the model of the articulated bodies in flight phase without any contact with environment, we apply the holonomic constraints at contact points and develop a constrained dynamics model of the robot in both single and double support phases. For the model with compliant contact, we propose a novel nonlinear contact model and simulate motion of the robot using this model. In order to show the performance of the developed models, we compare obtained results from these models to the empirical measurements from bipedal walking of the human-size humanoid robot SURENA III, which has been designed and fabricated at CAST, University of Tehran. This analysis shows the merit of both models in estimating dynamic behavior of the robot walking on a semi-rigid surface. The model with rigid contact, which is less complex and independent of the physical properties of the contacting bodies, can be employed for model-based motion optimization, analysis as well as control, while the model with compliant contact and more complexity is suitable for more realistic simulation scenarios. Copyright © 2017, The Authors. All rights reserved.

关键词： Robot programming

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