Feasible Region: An Actuation-Aware Extension of the Support Region

Orsolino, Romeo; Focchi, Michele; Caron, Stéphane; Raiola, Gennaro; Barasuol, Victor; Caldwell, Darwin G.; Semini, Claudio

doi:10.1109/tro.2020.2983318

Cited by 30 publications

(34 citation statements)

References 35 publications

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“…Links’ length and mass distribution, joint strengths and range of motions, ground contact modeling, and stance-specific constraints are specified and the subject’s dynamics can then be described using common robotic modeling approaches for floating-base robotic systems with multiple degrees-of-freedom (DOF) ( Figure 4A ). The dynamics and stability of systems in multi-contact stances is usually more challenging to describe ( Del Prete et al, 2018 ; Orsolino et al, 2020 ), given the indeterminacy in the system’s foot reactions when the legs form a closed loop with the ground ( Mummolo et al, 2015a ). Different modeling choices (e.g., number of DOF, single vs. double stance, planar vs. three-dimensional) lead to different BoB and balanced regions.…”

Section: Simultaneous Balance Assessment and Training Methodsmentioning

confidence: 99%

A Computational Framework Towards the Tele-Rehabilitation of Balance Control Skills

Akbaş

Mummolo

2021

Front. Robot. AI

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Mobility has been one of the most impacted aspects of human life due to the spread of the COVID-19 pandemic. Home confinement, the lack of access to physical rehabilitation, and prolonged immobilization of COVID-19-positive patients within hospitals are three major factors that affected the mobility of the general population world-wide. Balance is one key indicator to monitor the possible movement disorders that may arise both during the COVID-19 pandemic and in the coming future post-COVID-19. A systematic quantification of the balance performance in the general population is essential for preventing the appearance and progression of certain diseases (e.g., cardiovascular, neurodegenerative, and musculoskeletal), as well as for assessing the therapeutic outcomes of prescribed physical exercises for elderly and pathological patients. Current research on clinical exercises and associated outcome measures of balance is still far from reaching a consensus on a “golden standard” practice. Moreover, patients are often reluctant or unable to follow prescribed exercises, because of overcrowded facilities, lack of reliable and safe transportation, or stay-at-home orders due to the current pandemic. A novel balance assessment methodology, in combination with a home-care technology, can overcome these limitations. This paper presents a computational framework for the in-home quantitative assessment of balance control skills. Novel outcome measures of balance performance are implemented in the design of rehabilitation exercises with customized and quantifiable training goals. Using this framework in conjunction with a portable technology, physicians can treat and diagnose patients remotely, with reduced time and costs and a highly customized approach. The methodology proposed in this research can support the development of innovative technologies for smart and connected home-care solutions for physical therapy rehabilitation.

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Section: Simultaneous Balance Assessment and Training Methodsmentioning

confidence: 99%

A Computational Framework Towards the Tele-Rehabilitation of Balance Control Skills

Akbaş

Mummolo

2021

Front. Robot. AI

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“…The centroidal dynamics does not account for kinematic or actuator torque limits. To partly remedy these limitations, extensions of the contact wrench cone to include torque limits have been proposed [55] or learning-based approaches have been introduced to represent more complex whole-body constraints into the centroidal model itself [56].…”

Section: Centroidal Dynamicsmentioning

confidence: 99%

Recent Progress in Legged Robots Locomotion Control

Carpentier

Wieber

2021

Curr Robot Rep

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Purpose of review In recent years, legged robots locomotion has been transitioning from mostly flat ground in controlled settings to generic indoor and outdoor environments, approaching now real industrial scenarios. This paper aims at documenting some of the key progress made in legged locomotion control that enabled this transition.Recent findings Legged locomotion control makes extensive use of numerical trajectory optimization and its online implementation, model predictive control. A key progress has been how this optimization is handled, with refined models and refined numerical methods. This led the legged locomotion research community to heavily invest in and contribute to the development of new optimization methods and efficient numerical software. SummaryWe present an overview of the typical approach to legged locomotion control, which involves primarily planning a sequence of contacts with the environment and computing a corresponding dynamically feasible trajectory of the Center of Mass of the robot. We then detail recent progress in contact planning and trajectory optimization with either the full Lagrangian dynamics of legged robots or with reduced models.

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“…To the authors' knowledge, related works on impact or task-space model-predictive control did not consider the robot's structural hardware limits defined in joint-space, which implicitly represent bounds in the contact-space. A first step in this direction has been proposed in [26] for a reduced model, and later in [27], [28] for a non-redundant three degrees of freedom robot leg without considering impacts. Yet, none of these related works is explicitly aware of (or tries to optimize) the maximum possible impact velocity.…”

Section: Related Workmentioning

confidence: 99%

Robot-Safe Impacts with Soft Contacts Based on Learned Deformations

Dehio

Kheddar

2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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Safely generating impacts with robots is challenging due to subsequent discontinuous velocity and high impact forces. We aim at increasing the impact velocity -the robot's relative speed prior to contact -such that impact-tasks like grabbing and boxing are made with the highest allowable speed performance when needed. Previous works addressed this problem for rigid bodies' impacts. This letter proposes a control paradigm for generating intentional impacts with deformable contacts that incorporates hardware and task constraints. Based on data-driven learning of the shock-absorbing soft dynamics and a novel mapping of joint-space limits to contact-space, we devise a constrained model-predictive control to maximize the intentional impact within a feasible, robot-safe level. Our approach is assessed with real-robot experiments on the redundant Panda manipulator, demonstrating high pre-impact velocities (up to 0.9 m/s) of a rigid end-effector on soft objects and an end-effector soft suction-pump on rigid or deformable objects.

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Feasible Region: An Actuation-Aware Extension of the Support Region

Cited by 30 publications

References 35 publications

A Computational Framework Towards the Tele-Rehabilitation of Balance Control Skills

A Computational Framework Towards the Tele-Rehabilitation of Balance Control Skills

Recent Progress in Legged Robots Locomotion Control

Robot-Safe Impacts with Soft Contacts Based on Learned Deformations

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