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Article: The Neurorobotics Platform Robot Designer: Modeling Morphologies for Embodied Learning Experiments.

Feldotto, Benedikt / Morin, Fabrice O / Knoll, Alois

2022 Volume 16, Page(s) 856727

Abstract: The more we investigate the principles of motion learning in biological systems, the more we reveal the central role that body morphology plays in motion execution. Not only does anatomy define the kinematics and therefore the complexity of possible ... ...

Abstract	The more we investigate the principles of motion learning in biological systems, the more we reveal the central role that body morphology plays in motion execution. Not only does anatomy define the kinematics and therefore the complexity of possible movements, but it now becomes clear that part of the computation required for motion control is offloaded to body dynamics (a phenomenon referred to as "Morphological Computation.") Consequentially, a proper design of body morphology is essential to carry out meaningful simulations on motor control of robotic and musculoskeletal systems. The design should not be fixed for simulation experiments beforehand, but is a central research aspect in every motion learning experiment that requires continuous adaptation during the experimental phase. We herein introduce a plugin for the 3D modeling suite Blender that enables researchers to design morphologies for simulation experiments in, particularly but not restricted to, the Neurorobotics Platform. We include design capabilities for both musculoskeletal bodies, as well as robotic systems in the Robot Designer. Thereby, we hope to not only foster understanding of biological motions and enabling better robot designs, but enabling true Neurorobotic experiments that may consist of biomimetic models such as tendon-driven robot as a mix of both or a transition between both biology and technology. This plugin helps researchers design and parameterize models with a Graphical User Interface and thus simplifies and speeds up the overall design process.
Language	English
Publishing date	2022-04-25
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2453002-5
ISSN	1662-5218
ISSN	1662-5218
DOI	10.3389/fnbot.2022.856727
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Article ; Online: Meta-Reinforcement Learning in Nonstationary and Nonparametric Environments.

Bing, Zhenshan / Knak, Lukas / Cheng, Long / Morin, Fabrice O / Huang, Kai / Knoll, Alois

IEEE transactions on neural networks and learning systems

2023 Volume PP

Abstract: Recent state-of-the-art artificial agents lack the ability to adapt rapidly to new tasks, as they are trained exclusively for specific objectives and require massive amounts of interaction to learn new skills. Meta-reinforcement learning (meta-RL) ... ...

Abstract	Recent state-of-the-art artificial agents lack the ability to adapt rapidly to new tasks, as they are trained exclusively for specific objectives and require massive amounts of interaction to learn new skills. Meta-reinforcement learning (meta-RL) addresses this challenge by leveraging knowledge learned from training tasks to perform well in previously unseen tasks. However, current meta-RL approaches limit themselves to narrow parametric and stationary task distributions, ignoring qualitative differences and nonstationary changes between tasks that occur in the real world. In this article, we introduce a Task-Inference-based meta-RL algorithm using explicitly parameterized Gaussian variational autoencoders (VAEs) and gated Recurrent units (TIGR), designed for nonparametric and nonstationary environments. We employ a generative model involving a VAE to capture the multimodality of the tasks. We decouple the policy training from the task-inference learning and efficiently train the inference mechanism on the basis of an unsupervised reconstruction objective. We establish a zero-shot adaptation procedure to enable the agent to adapt to nonstationary task changes. We provide a benchmark with qualitatively distinct tasks based on the half-cheetah environment and demonstrate the superior performance of TIGR compared with state-of-the-art meta-RL approaches in terms of sample efficiency (three to ten times faster), asymptotic performance, and applicability in nonparametric and nonstationary environments with zero-shot adaptation. Videos can be viewed at https://videoviewsite.wixsite.com/tigr.
Language	English
Publishing date	2023-05-24
Publishing country	United States
Document type	Journal Article
ISSN	2162-2388
ISSN (online)	2162-2388
DOI	10.1109/TNNLS.2023.3270298
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Article ; Online: Robotic Manipulation in Dynamic Scenarios via Bounding-Box-Based Hindsight Goal Generation.

Bing, Zhenshan / Alvarez, Erick / Cheng, Long / Morin, Fabrice O / Li, Rui / Su, Xiaojie / Huang, Kai / Knoll, Alois

IEEE transactions on neural networks and learning systems

2023 Volume 34, Issue 8, Page(s) 5037–5050

Abstract: By relabeling past experience with heuristic or curriculum goals, state-of-the-art reinforcement learning (RL) algorithms such as hindsight experience replay (HER), hindsight goal generation (HGG), and graph-based HGG (G-HGG) have been able to solve ... ...

Abstract	By relabeling past experience with heuristic or curriculum goals, state-of-the-art reinforcement learning (RL) algorithms such as hindsight experience replay (HER), hindsight goal generation (HGG), and graph-based HGG (G-HGG) have been able to solve challenging robotic manipulation tasks in multigoal settings with sparse rewards. HGG outperforms HER in challenging tasks in which goals are difficult to explore by learning from a curriculum, in which intermediate goals are selected based on the Euclidean distance to target goals. G-HGG enhances HGG by selecting intermediate goals from a precomputed graph representation of the environment, which enables its applicability in an environment with stationary obstacles. However, G-HGG is not applicable to manipulation tasks with dynamic obstacles, since its graph representation is only valid in static scenarios and fails to provide any correct information to guide the exploration. In this article, we propose bounding-box-based HGG (Bbox-HGG), an extension of G-HGG selecting hindsight goals with the help of image observations of the environment, which makes it applicable to tasks with dynamic obstacles. We evaluate Bbox-HGG on four challenging manipulation tasks, where significant enhancements in both sample efficiency and overall success rate are shown over state-of-the-art algorithms. The videos can be viewed at https://videoviewsite.wixsite.com/bbhgg.
Language	English
Publishing date	2023-08-04
Publishing country	United States
Document type	Journal Article
ISSN	2162-2388
ISSN (online)	2162-2388
DOI	10.1109/TNNLS.2021.3124366
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Article ; Online: Lateral flexion of a compliant spine improves motor performance in a bioinspired mouse robot.

Bing, Zhenshan / Rohregger, Alex / Walter, Florian / Huang, Yuhong / Lucas, Peer / Morin, Fabrice O / Huang, Kai / Knoll, Alois

Science robotics

2023 Volume 8, Issue 85, Page(s) eadg7165

Abstract: A flexible spine is critical to the motion capability of most animals and plays a pivotal role in their agility. Although state-of-the-art legged robots have already achieved very dynamic and agile movement solely relying on their legs, they still ... ...

Abstract	A flexible spine is critical to the motion capability of most animals and plays a pivotal role in their agility. Although state-of-the-art legged robots have already achieved very dynamic and agile movement solely relying on their legs, they still exhibit the type of stiff movement that compromises movement efficiency. The integration of a flexible spine thus appears to be a promising approach to improve their agility, especially for small and underactuated quadruped robots that are underpowered because of size limitations. Here, we show that the lateral flexion of a compliant spine can promote both walking speed and maneuver agility for a neurorobotic mouse (NeRmo). We present NeRmo as a biomimetic robotic mouse that mimics the morphology of biological mice and their muscle-tendon actuation system. First, by leveraging the lateral flexion of the compliant spine, NeRmo can greatly increase its static stability in an initially unstable configuration by adjusting its posture. Second, the lateral flexion of the spine can also effectively extend the stride length of a gait and therefore improve the walking speeds of NeRmo. Finally, NeRmo shows agile maneuvers that require both a small turning radius and fast walking speed with the help of the spine. These results advance our understanding of spine-based quadruped locomotion skills and highlight promising design concepts to develop more agile legged robots.
MeSH term(s)	Animals ; Mice ; Robotics/methods ; Gait ; Movement ; Posture ; Motion
Language	English
Publishing date	2023-12-06
Publishing country	United States
Document type	Journal Article
ISSN	2470-9476
ISSN (online)	2470-9476
DOI	10.1126/scirobotics.adg7165
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Article ; Online: Complex Robotic Manipulation via Graph-Based Hindsight Goal Generation.

Bing, Zhenshan / Brucker, Matthias / Morin, Fabrice O / Li, Rui / Su, Xiaojie / Huang, Kai / Knoll, Alois

IEEE transactions on neural networks and learning systems

2022 Volume 33, Issue 12, Page(s) 7863–7876

Abstract: Reinforcement learning algorithms, such as hindsight experience replay (HER) and hindsight goal generation (HGG), have been able to solve challenging robotic manipulation tasks in multigoal settings with sparse rewards. HER achieves its training success ... ...

Abstract	Reinforcement learning algorithms, such as hindsight experience replay (HER) and hindsight goal generation (HGG), have been able to solve challenging robotic manipulation tasks in multigoal settings with sparse rewards. HER achieves its training success through hindsight replays of past experience with heuristic goals but underperforms in challenging tasks in which goals are difficult to explore. HGG enhances HER by selecting intermediate goals that are easy to achieve in the short term and promising to lead to target goals in the long term. This guided exploration makes HGG applicable to tasks in which target goals are far away from the object's initial position. However, the vanilla HGG is not applicable to manipulation tasks with obstacles because the Euclidean metric used for HGG is not an accurate distance metric in such an environment. Although, with the guidance of a handcrafted distance grid, grid-based HGG can solve manipulation tasks with obstacles, a more feasible method that can solve such tasks automatically is still in demand. In this article, we propose graph-based hindsight goal generation (G-HGG), an extension of HGG selecting hindsight goals based on shortest distances in an obstacle-avoiding graph, which is a discrete representation of the environment. We evaluated G-HGG on four challenging manipulation tasks with obstacles, where significant enhancements in both sample efficiency and overall success rate are shown over HGG and HER. Videos can be viewed at https://videoviewsite.wixsite.com/ghgg.
Language	English
Publishing date	2022-11-30
Publishing country	United States
Document type	Journal Article
ISSN	2162-2388
ISSN (online)	2162-2388
DOI	10.1109/TNNLS.2021.3088947
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Article ; Online: Toward Cognitive Navigation: Design and Implementation of a Biologically Inspired Head Direction Cell Network.

Bing, Zhenshan / Sewisy, Amir Ei / Zhuang, Genghang / Walter, Florian / Morin, Fabrice O / Huang, Kai / Knoll, Alois

IEEE transactions on neural networks and learning systems

2022 Volume 33, Issue 5, Page(s) 2147–2158

Abstract: As a vital cognitive function of animals, the navigation skill is first built on the accurate perception of the directional heading in the environment. Head direction cells (HDCs), found in the limbic system of animals, are proven to play an important ... ...

Abstract	As a vital cognitive function of animals, the navigation skill is first built on the accurate perception of the directional heading in the environment. Head direction cells (HDCs), found in the limbic system of animals, are proven to play an important role in identifying the directional heading allocentrically in the horizontal plane, independent of the animal's location and the ambient conditions of the environment. However, practical HDC models that can be implemented in robotic applications are rarely investigated, especially those that are biologically plausible and yet applicable to the real world. In this article, we propose a computational HDC network that is consistent with several neurophysiological findings concerning biological HDCs and then implement it in robotic navigation tasks. The HDC network keeps a representation of the directional heading only relying on the angular velocity as an input. We examine the proposed HDC model in extensive simulations and real-world experiments and demonstrate its excellent performance in terms of accuracy and real-time capability.
MeSH term(s)	Animals ; Cognition ; Neural Networks, Computer
Language	English
Publishing date	2022-05-02
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	2162-2388
ISSN (online)	2162-2388
DOI	10.1109/TNNLS.2021.3128380
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Article ; Online: Chemotactic cell migration: the core autophagy protein ATG9A is at the leading edge.

Campisi, Daniele / Desrues, Laurence / Dembélé, Kleouforo-Paul / Mutel, Alexandre / Parment, Renaud / Gandolfo, Pierrick / Castel, Hélène / Morin, Fabrice

Autophagy

2022 Volume 18, Issue 12, Page(s) 3037–3039

Abstract: Accumulating data indicate that several components of the macroautophagy/autophagy machinery mediate additional functions, which do not depend on autophagosome biogenesis or lysosomal cargo degradation. In this context, we found that the core autophagy ... ...

Abstract	Accumulating data indicate that several components of the macroautophagy/autophagy machinery mediate additional functions, which do not depend on autophagosome biogenesis or lysosomal cargo degradation. In this context, we found that the core autophagy protein ATG9A participates in the chemotactic movement of several cell lines, including highly invasive glioblastoma cells. Accordingly, ATG9A-depleted cells are unable to form large and persistent leading-edge protrusions. By the design of an ATG9A-pHluorin construct and TIRF imaging, we established that ATG9A-positive vesicles are targeted toward the migration front, where their exocytosis is synchronized with protrusive activity. We finally demonstrated that ATG9A, through its interaction with clathrin adaptor complexes, controls the delivery of ITGB1 (integrin subunit beta 1) to the migration front and normal adhesion dynamics. Together, our work indicates that ATG9A protein has a wider role than anticipated and constitutes a critical component of vesicular trafficking allowing the expansion of cell protrusions and their anchorage to the extracellular matrix.
MeSH term(s)	Autophagy-Related Proteins/metabolism ; Autophagy ; Vesicular Transport Proteins/metabolism ; Membrane Proteins/metabolism ; Cell Movement
Chemical Substances	Autophagy-Related Proteins ; Vesicular Transport Proteins ; Membrane Proteins
Language	English
Publishing date	2022-04-29
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2454135-7
ISSN	1554-8635 ; 1554-8627
ISSN (online)	1554-8635
ISSN	1554-8627
DOI	10.1080/15548627.2022.2069903
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Article ; Online: The core autophagy protein ATG9A controls dynamics of cell protrusions and directed migration.

Campisi, Daniele / Desrues, Laurence / Dembélé, Kléouforo-Paul / Mutel, Alexandre / Parment, Renaud / Gandolfo, Pierrick / Castel, Hélène / Morin, Fabrice

The Journal of cell biology

2022 Volume 221, Issue 3

Abstract: Chemotactic migration is a fundamental cellular behavior relying on the coordinated flux of lipids and cargo proteins toward the leading edge. We found here that the core autophagy protein ATG9A plays a critical role in the chemotactic migration of ... ...

Abstract	Chemotactic migration is a fundamental cellular behavior relying on the coordinated flux of lipids and cargo proteins toward the leading edge. We found here that the core autophagy protein ATG9A plays a critical role in the chemotactic migration of several human cell lines, including highly invasive glioma cells. Depletion of ATG9A protein altered the formation of large and persistent filamentous actin (F-actin)-rich lamellipodia that normally drive directional migration. Using live-cell TIRF microscopy, we demonstrated that ATG9A-positive vesicles are targeted toward the migration front of polarized cells, where their exocytosis correlates with protrusive activity. Finally, we found that ATG9A was critical for efficient delivery of β1 integrin to the leading edge and normal adhesion dynamics. Collectively, our data uncover a new function for ATG9A protein and indicate that ATG9A-positive vesicles are mobilized during chemotactic stimulation to facilitate expansion of the lamellipodium and its anchorage to the extracellular matrix.
MeSH term(s)	Actins/metabolism ; Autophagy ; Autophagy-Related Proteins/metabolism ; Cell Adhesion ; Cell Line, Tumor ; Cell Movement ; Cell Surface Extensions/metabolism ; Chemotaxis ; Exocytosis ; Green Fluorescent Proteins ; Humans ; Integrin beta1/metabolism ; Membrane Glycoproteins/metabolism ; Membrane Proteins/metabolism ; Pseudopodia/metabolism ; Reproducibility of Results ; Vesicular Transport Proteins/metabolism
Chemical Substances	Actins ; ATG9A protein, human ; Autophagy-Related Proteins ; Integrin beta1 ; Membrane Glycoproteins ; Membrane Proteins ; PHluorin ; TGOLN2 protein, human ; Vesicular Transport Proteins ; Green Fluorescent Proteins (147336-22-9)
Language	English
Publishing date	2022-02-18
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	218154-x
ISSN	1540-8140 ; 0021-9525
ISSN (online)	1540-8140
ISSN	0021-9525
DOI	10.1083/jcb.202106014
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Article: Embodied bidirectional simulation of a spiking cortico-basal ganglia-cerebellar-thalamic brain model and a mouse musculoskeletal body model distributed across computers including the supercomputer Fugaku.

Kuniyoshi, Yusuke / Kuriyama, Rin / Omura, Shu / Gutierrez, Carlos Enrique / Sun, Zhe / Feldotto, Benedikt / Albanese, Ugo / Knoll, Alois C / Yamada, Taiki / Hirayama, Tomoya / Morin, Fabrice O / Igarashi, Jun / Doya, Kenji / Yamazaki, Tadashi

Frontiers in neurorobotics

2023 Volume 17, Page(s) 1269848

Abstract: Embodied simulation with a digital brain model and a realistic musculoskeletal body model provides a means to understand animal behavior and behavioral change. Such simulation can be too large and complex to conduct on a single computer, and so ... ...

Abstract	Embodied simulation with a digital brain model and a realistic musculoskeletal body model provides a means to understand animal behavior and behavioral change. Such simulation can be too large and complex to conduct on a single computer, and so distributed simulation across multiple computers over the Internet is necessary. In this study, we report our joint effort on developing a spiking brain model and a mouse body model, connecting over the Internet, and conducting bidirectional simulation while synchronizing them. Specifically, the brain model consisted of multiple regions including secondary motor cortex, primary motor and somatosensory cortices, basal ganglia, cerebellum and thalamus, whereas the mouse body model, provided by the Neurorobotics Platform of the Human Brain Project, had a movable forelimb with three joints and six antagonistic muscles to act in a virtual environment. Those were simulated in a distributed manner across multiple computers including the supercomputer Fugaku, which is the flagship supercomputer in Japan, while communicating via Robot Operating System (ROS). To incorporate models written in C/C++ in the distributed simulation, we developed a C++ version of the rosbridge library from scratch, which has been released under an open source license. These results provide necessary tools for distributed embodied simulation, and demonstrate its possibility and usefulness toward understanding animal behavior and behavioral change.
Language	English
Publishing date	2023-10-05
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2453002-5
ISSN	1662-5218
ISSN	1662-5218
DOI	10.3389/fnbot.2023.1269848
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Article: The Autophagy Machinery: A New Player in Chemotactic Cell Migration.

Coly, Pierre-Michaël / Gandolfo, Pierrick / Castel, Hélène / Morin, Fabrice

Frontiers in neuroscience

2017 Volume 11, Page(s) 78

Abstract: Autophagy is a highly conserved self-degradative process that plays a key role in diverse cellular processes such as stress response or differentiation. A growing body of work highlights the direct involvement of autophagy in cell migration and cancer ... ...

Abstract	Autophagy is a highly conserved self-degradative process that plays a key role in diverse cellular processes such as stress response or differentiation. A growing body of work highlights the direct involvement of autophagy in cell migration and cancer metastasis. Specifically, autophagy has been shown to be involved in modulating cell adhesion dynamics as well as epithelial-to-mesenchymal transition. After providing a general overview of the mechanisms controlling autophagosome biogenesis and cell migration, we discuss how chemotactic G protein-coupled receptors, through the repression of autophagy, may orchestrate membrane trafficking and compartmentation of specific proteins at the cell front in order to support the critical steps of directional migration.
Language	English
Publishing date	2017-02-16
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2411902-7
ISSN	1662-453X ; 1662-4548
ISSN (online)	1662-453X
ISSN	1662-4548
DOI	10.3389/fnins.2017.00078
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