3,235 research outputs found
On Neuromechanical Approaches for the Study of Biological Grasp and Manipulation
Biological and robotic grasp and manipulation are undeniably similar at the
level of mechanical task performance. However, their underlying fundamental
biological vs. engineering mechanisms are, by definition, dramatically
different and can even be antithetical. Even our approach to each is
diametrically opposite: inductive science for the study of biological systems
vs. engineering synthesis for the design and construction of robotic systems.
The past 20 years have seen several conceptual advances in both fields and the
quest to unify them. Chief among them is the reluctant recognition that their
underlying fundamental mechanisms may actually share limited common ground,
while exhibiting many fundamental differences. This recognition is particularly
liberating because it allows us to resolve and move beyond multiple paradoxes
and contradictions that arose from the initial reasonable assumption of a large
common ground. Here, we begin by introducing the perspective of neuromechanics,
which emphasizes that real-world behavior emerges from the intimate
interactions among the physical structure of the system, the mechanical
requirements of a task, the feasible neural control actions to produce it, and
the ability of the neuromuscular system to adapt through interactions with the
environment. This allows us to articulate a succinct overview of a few salient
conceptual paradoxes and contradictions regarding under-determined vs.
over-determined mechanics, under- vs. over-actuated control, prescribed vs.
emergent function, learning vs. implementation vs. adaptation, prescriptive vs.
descriptive synergies, and optimal vs. habitual performance. We conclude by
presenting open questions and suggesting directions for future research. We
hope this frank assessment of the state-of-the-art will encourage and guide
these communities to continue to interact and make progress in these important
areas
Enactivism, other minds, and mental disorders
Although enactive approaches to cognition vary in terms of their character and scope, all endorse several core claims. The first is that cognition is tied to action. The second is that cognition is composed of more than just in-the-head processes; cognitive activities are externalized via features of our embodiment and in our ecological dealings with the people and things around us. I appeal to these two enactive claims to consider a view called “direct social perception” : the idea that we can sometimes perceive features of other minds directly in the character of their embodiment and environmental interactions. I argue that if DSP is true, we can probably also perceive certain features of mental disorders as well. I draw upon the developmental psychologist Daniel Stern’s notion of “forms of vitality”—largely overlooked in these debates—to develop this idea, and I use autism as a case study. I argue further that an enactive approach to DSP can clarify some ways we play a regulative role in shaping the temporal and phenomenal character of the disorder in question, and it may therefore have practical significance for both the clinical and therapeutic encounter
Role of Kinematics Assessment and Multimodal Sensorimotor Training for Motion Deficits in Breast Cancer Chemotherapy-Induced Polyneuropathy: A Perspective on Virtual Reality Avatars
Chemotherapy-induced polyneuropathy (CIPN), one of the most severe and incapacitating side effects of chemotherapeutic drugs, is a serious concern in breast cancer therapy leading to dose diminution, delay, or cessation. The reversibility of CIPN is of increasing importance since active chemotherapies prolong survival. Clinical assessment tools show that patients experiencing sensorimotor CIPN symptoms not only do they have to cope with loss in autonomy and life quality, but CIPN has become a key restricting factor in treatment. CIPN incidence poses a clinical challenge and has lacked established and efficient therapeutic options up to now. Complementary, non-opioid therapies are sought for both prevention and management of CIPN. In this perspective, we explore the potential that digital interventions have for sensorimotor CIPN rehabilitation in breast cancer patients. Our primary goal is to emphasize the benefits and impact that Virtual Reality (VR) avatars and Machine Learning have in combination in a digital intervention aiming at (1) assessing the complete kinematics of deficits through learning underlying patient sensorimotor parameters, and (2) parameterize a multimodal VR simulation to drive personalized deficit compensation. We support our perspective by evaluating sensorimotor effects of chemotherapy, the metrics to assess sensorimotor deficits, and relevant clinical studies. We subsequently analyse the neurological substrate of VR sensorimotor rehabilitation, with multisensory integration acting as a key element. Finally, we propose a closed-loop patient-centered design recommendation for CIPN sensorimotor rehabilitation. Our aim is to provoke the scientific community toward the development and use of such digital interventions for more efficient and targeted rehabilitation
Visual Prediction of Priors for Articulated Object Interaction
Exploration in novel settings can be challenging without prior experience in
similar domains. However, humans are able to build on prior experience quickly
and efficiently. Children exhibit this behavior when playing with toys. For
example, given a toy with a yellow and blue door, a child will explore with no
clear objective, but once they have discovered how to open the yellow door,
they will most likely be able to open the blue door much faster. Adults also
exhibit this behavior when entering new spaces such as kitchens. We develop a
method, Contextual Prior Prediction, which provides a means of transferring
knowledge between interactions in similar domains through vision. We develop
agents that exhibit exploratory behavior with increasing efficiency, by
learning visual features that are shared across environments, and how they
correlate to actions. Our problem is formulated as a Contextual Multi-Armed
Bandit where the contexts are images, and the robot has access to a
parameterized action space. Given a novel object, the objective is to maximize
reward with few interactions. A domain which strongly exhibits correlations
between visual features and motion is kinemetically constrained mechanisms. We
evaluate our method on simulated prismatic and revolute joints.Comment: IEEE International Conference on Robotics and Automation (ICRA) 202
Robust and adaptive door operation with a mobile robot
The version of record is available online at: http://dx.doi.org/10.1007/s11370-021-00366-7The ability to deal with articulated objects is very important for robots assisting humans. In this work, a framework to robustly and adaptively operate common doors, using an autonomous mobile manipulator, is proposed. To push forward the state of the art in robustness and speed performance, we devise a novel algorithm that fuses a convolutional neural network with efficient point cloud processing. This advancement enables real-time grasping pose estimation for multiple handles from RGB-D images, providing a speed up improvement for assistive human-centered applications. In addition, we propose a versatile Bayesian framework that endows the robot with the ability to infer the door kinematic model from observations of its motion and learn from previous experiences or human demonstrations. Combining these algorithms with a Task Space Region motion planner, we achieve an efficient door operation regardless of the kinematic model. We validate our framework with real-world experiments using the Toyota human support robot.Peer ReviewedPostprint (author's final draft
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