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learning to Control Redundant Musculoskeletal Systems with Neural Networks and SQP: Exploiting Muscle Properties

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Learning to Control Redundant Musculoskeletal Systems with N...

IEEE International Conference on robotics and Automation

作者： Danny Driess Heiko Zimmermann Simon Wolfen Dan Suissa Daniel Haeufle Daniel Hennes Marc Toussaint Syn Schmitt Machine Learning and Robotics Lab University of Stuttgart Germany Biomechanics and Biorobotics Group University of Stuttgart Germany Multi-Level Modeling in Motor Control and Rehabilitation Robotics University of Tübingen Germany

Modeling biomechanical musculoskeletal systems reveals that the mapping from muscle stimulations to movement dynamics is highly nonlinear and complex, which makes it difficult to control those systems with classical techniques. In this work, we not only investigate whether machine learning approaches are capable of learning a controller for such systems. We are especially interested in the question if the structure of the musculoskeletal apparatus exhibits properties that are favorable for the learning task. In particular, we consider learning a control policy from target positions to muscle stimulations. To account for the high actuator redundancy of biomechanical systems, our approach uses a learned forward model represented by a neural network and sequential quadratic programming to obtain the control policy, which also enables us to alternate the co-contraction level and hence allows to change the stiffness of the system and to include optimality criteria like small muscle stimulations. Experiments on both a simulated musculoskeletal model of a human arm and a real biomimetic muscle-driven robot show that our approach is able to learn an accurate controller despite high redundancy and nonlinearity, while retaining sample efficiency.

关键词： Muscles Joints Robots Biological system modeling Torque Biomechanics

MoGaze: A dataset of full-body motions that includes workspace geometry and eye-gaze

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arXiv 2020年

作者： Kratzer, Philipp Bihlmaier, Simon Midlagajni, Niteesh Balachandra Prakash, Rohit Toussaint, Marc Mainprice, Jim Machine Learning and Robotics Lab University of Stuttgart Humans to Robots Motions Research Group University of Stuttgart Germany Humans to Robots Motions Research Group University of Stuttgart Germany Learning and Intelligent Systems lab TU Berlin Germany

As robots become more present in open human environments, it will become crucial for robotic systems to understand and predict human motion. Such capabilities depend heavily on the quality and availability of motion capture data. However, existing datasets of full-body motion rarely include 1) long sequences of manipulation tasks, 2) the 3D model of the workspace geometry, and 3) eye-gaze, which are all important when a robot needs to predict the movements of humans in close proximity. Hence, in this paper, we present a novel dataset of full-body motion for everyday manipulation tasks, which includes the above. The motion data was captured using a traditional motion capture system based on reflective markers. We additionally captured eye-gaze using a wearable pupil-tracking device. As we show in experiments, the dataset can be used for the design and evaluation of full-body motion prediction algorithms. Furthermore, our experiments show eye-gaze as a powerful predictor of human intent. The dataset includes 180 min of motion capture data with 1627 pick and place actions being performed. It is available at MoGaze Dataset1 and is planned to be extended to collaborative tasks with two humans in the near future. Copyright © 2020, The Authors. All rights reserved.

关键词： Motion estimation

Deep 6-DoF tracking of unknown objects for reactive grasping

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arXiv 2021年

作者： Tuscher, Marc Hörz, Julian Driess, Danny Toussaint, Marc Sereact Germany Machine Learning and Robotics Lab University of Stuttgart Germany Max-Planck Institute for Intelligent Systems Stuttgart Germany Learning and Intelligent Systems TU Berlin Germany

Robotic manipulation of unknown objects is an important field of research. Practical applications occur in many real-world settings where robots need to interact with an unknown environment. We tackle the problem of reactive grasping by proposing a method for unknown object tracking, grasp point sampling and dynamic trajectory planning. Our object tracking method combines Siamese Networks with an Iterative Closest Point approach for pointcloud registration into a method for 6-DoF unknown object tracking. The method does not require further training and is robust to noise and occlusion. We propose a robotic manipulation system, which is able to grasp a wide variety of formerly unseen objects and is robust against object perturbations and inferior grasping points. © 2021, CC BY.

关键词： Iterative methods

Effect of prehistory on the ambiguous stimuli processing in the human brain 5

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Effect of prehistory on the ambiguous stimuli processing in ...

5th Scientific School on Dynamics of Complex Networks and their Applications, DCNA 2021

作者： Kuc, Alexander Neuroscience and Cognitive Technology Lab Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University Kaliningrad Russia

ISBN: (纸本)9781665442824

To model the picture of the external environment, the brain uses data coming from the sensory system. However, it is believed that the brain’s representation of the external environment is formed not only by sensory information, but also by a priori knowledge, the so-called predictions. This work considers how the previous sensory information (sensory prehistory) influences the processing of the current visual stimuli. Bistable images of the Necker cube with varying degrees of ambiguity were used as visual stimuli. We found EEG biomarkers possibly reflecting the effect of prehistory on the brain state and the ongoing stimulus processing. © 2021 IEEE

关键词： Visualization Complex networks Biomarkers Brain modeling Electroencephalography Data models Time factors

Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly

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arXiv 2021年

作者： Hartmann, Valentin N. Orthey, Andreas Driess, Danny Oguz, Ozgur S. Toussaint, Marc Machine Learning & Robotics Lab University of Stuttgart Germany Learning and Intelligent Systems Group TU Berlin Germany Department of Computer Engineering Bilkent University Turkey

Robotic assembly planning enables architects to explicitly account for the assembly process during the design phase, and enables efficient building methods that profit from the robots' different capabilities. Previous work has addressed planning of robot assembly sequences and identifying the feasibility of architectural designs. This paper extends previous work by enabling planning with large, heterogeneous teams of robots. We present a planning system which enables parallelization of complex task and motion planning problems by iteratively solving smaller subproblems. Combining optimization methods to solve for manipulation constraints with a sampling-based bi-directional space-time path planner enables us to plan cooperative multirobot manipulation with unknown arrival-times. Thus, our solver allows for completing subproblems and tasks with differing timescales and synchronizes them effectively. We demonstrate the approach on multiple case-studies to show the robustness over long planning horizons and scalability to many objects and agents of our algorithm. Finally, we also demonstrate the execution of the computed plans on two robot arms to showcase the feasibility in the real world. Copyright © 2021, The Authors. All rights reserved.

关键词： robotics

An Interior Point Method Solving Motion Planning Problems with Narrow Passages

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arXiv 2020年

作者： Mainprice, Jim Ratliff, Nathan Toussaint, Marc Schaal, Stefan Machine Learning and Robotics Lab University of Stuttgart Germany Max Planck Institute for Intelligent Systems IS-MPITübingen & Stuttgart Germany Learning and Intelligent Systems Lab TU Berlin Berlin Germany

Algorithmic solutions for the motion planning problem have been investigated for five decades. Since the development of A* in 1969 many approaches have been investigated, traditionally classified as either grid decomposition, potential fields or sampling-based. In this work, we focus on using numerical optimization, which is understudied for solving motion planning problems. This lack of interest in the favor of sampling-based methods is largely due to the non-convexity introduced by narrow passages. We address this shortcoming by grounding the solution in differential geometry. We demonstrate through a series of experiments on 3 Dofs and 6 Dofs narrow passage problems, how modeling explicitly the underlying Riemannian manifold leads to an efficient interior point non-linear programming solution. 1. Copyright © 2020, The Authors. All rights reserved.

关键词： Nonlinear programming

Robust Task and Motion Planning for Long-Horizon Architectural Construction Planning

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Robust Task and Motion Planning for Long-Horizon Architectur...

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Valentin N. Hartmann Ozgur S. Oguz Danny Driess Marc Toussaint Achim Menges Machine Learning & Robotics Lab University of Stuttgart Germany Max Planck Institute for Intelligent Systems Germany Institute for Computational Design and Construction University of Stuttgart Germany

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

Integrating robotic systems in architectural and construction processes is of core interest to increase the efficiency of the building industry. Automated planning for such systems enables design analysis tools and facilitates faster design iteration cycles for designers and engineers. However, generic task-and-motion planning (TAMP) for long-horizon construction processes is beyond the capabilities of current approaches. In this paper, we develop a multi-agent TAMP framework for long horizon problems such as constructing a full-scale building. To this end we extend the Logic-Geometric Programming framework by sampling-based motion planning, a limited horizon approach, and a task-specific structural stability optimization that allow an effective decomposition of the task. We show that our framework is capable of constructing a large pavilion built from several hundred geometrically unique building elements from start to end autonomously.

关键词： Buildings Tools Stability analysis Planning Structural engineering Task analysis Optimization

Motion prediction with recurrent neural network dynamical models and trajectory optimization

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arXiv 2019年

作者： Kratzer, Philipp Toussaint, Marc Mainprice, Jim Machine Learning and Robotics Lab University of Stuttgart Germany Max Planck Institute for Intelligent Systems IS-MPI Tübingen Germany

Predicting human motion in unstructured and dynamic environments is difficult as humans naturally exhibit complex behaviors that can change drastically from one environment to the next. In order to alleviate this issue, we propose to encode the lower level aspects of human motion separately from the higher level geometrical aspects, which we believe will generalize better over environments. In contrast to our prior work [6], we encode the short-term behavior by using a state-of-the-art recurrent neural network structure instead of a Gaussian process. In order to perform longer term behavior predictions that account for variation in tasks and environments, we propose to make use of gradient based trajectory optimization. Preliminary experiments on real motion data demonstrate the efficacy of the approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Recurrent neural networks

learning arbitration for shared autonomy by hindsight data aggregation

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arXiv 2019年

作者： Oh, Yoojin Toussaint, Marc Mainprice, Jim Machine Learning and Robotics Lab University of Stuttgart Germany Max Planck Institute for Intelligent Systems MPI-IS Tübingen Germany

In this paper we present a framework for the teleoperation of pick-and-place tasks. We define a shared control policy that allows to blend between direct user control and autonomous control based on user intent inference. One of the main challenges in shared autonomy systems is to define the arbitration function, which decides when to let the autonomous agent take over. In this work, we propose a model and training method to learn the arbitration function. Our model is based on a recurrent neural network that takes as input the state, intent prediction scores and user command to produce an arbitration between user and robot commands. This work extends our previous work on differentiable policies for shared autonomy [12]. Differentiability of the policy is desirable to further train the shared autonomy system end-to-end. In this work we propose training of the arbitration function by using data from user performing the task with shared control. We present initial results by teleoperating a gripper in a virtual environment using pre-trained motion generation and intent prediction. We compare our data aggregation training procedure to a handcrafted arbitration function. Our preliminary results show the efficacy of the approach and shed light on limitations that we believe demonstrate the need for user adaptability in shared autonomy systems. Copyright © 2019, The Authors. All rights reserved.

关键词： Autonomous agents