C. Della Santina
106 records found
1
Awareness in Robotics
An Early Perspective from the Viewpoint of the EIC Pathfinder Challenge “Awareness Inside”
While consciousness has been historically a heavily debated topic, awareness had less success in raising the interest of scholars. However, more and more researchers are getting interested in answering questions concerning what awareness is and how it can be artificially generate
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Stepping strategy, including step time and step location modulation, and hip strategy, i.e., upper-body movement, play crucial roles in achieving robust humanoid locomotion. However, exploiting these balance strategies in a unified and flexible manner has not been well addressed.
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Soft continuum manipulators are celebrated for their versatility and physical robustness to external forces and perturbations. However, this feature comes at a cost. The many degrees of freedom and compliance pose challenges for accurate pose reconstruction, both in terms of dist
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While much work has been done recently in the realm of model-based control of soft robots and soft-rigid hybrids, most works examine robots that have an inherently serial structure. While these systems have been prevalent in the literature, there is an increasing trend toward des
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PUMA
Deep Metric Imitation Learning for Stable Motion Primitives
Imitation learning (IL) facilitates intuitive robotic programming. However, ensuring the reliability of learned behaviors remains a challenge. In the context of reaching motions, a robot should consistently reach its goal, regardless of its initial conditions. To meet this requir
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BICEP
A Bio-Inspired Compliant Elbow Prosthesis
Adopting compliant structures holds the potential to enhance the robustness and interaction capabilities of the next generation of bionic limbs. Although researchers have proficiently explored this approach in the design of artificial hands, they devoted little attention to the d
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Parallel robots based on Handed Shearing Auxetics (HSAs) can implement complex motions using standard electric motors while maintaining the complete softness of the structure, thanks to specifically designed architected metamaterials. However, their control is especially challeng
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Soft robots enable safe and robust operations in unstructured environments. However, the nonlinearities of their continuum structure complicate the accomplishment of classic robotic tasks, such as pick and place. In this work, we propose the R-Soft Inverted Pendulum, a Soft Inver
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A central challenge in Learning from Demonstration is to generate representations that are adaptable and can generalize to unseen situations. This work proposes to learn such a representation without using task-specific heuristics within the context of multi-reference frame skill
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Robust Quadrupedal Jumping with Impact-Aware Landing
Exploiting Parallel Elasticity
Introducing parallel elasticity in the hardware design endows quadrupedal robots with the ability to perform explosive and efficient motions. However, for this kind of articulated soft quadruped, realizing dynamic jumping with robustness against system uncertainties remains a cha
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Physics-Informed Neural Networks to Model and Control Robots
A Theoretical and Experimental Investigation
This work concerns the application of physics-informed neural networks to the modeling and control of complex robotic systems. Achieving this goal requires extending physics-informed neural networks to handle nonconservative effects. These learned models are proposed to combine w
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This letter concerns control-oriented and structure-preserving learning of low-dimensional approximations of high-dimensional physical systems, with a focus on mechanical systems. We investigate the integration of neural autoencoders in model order reduction, while at the same ti
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Continuum soft robots are nonlinear mechanical systems with theoretically infinite degrees of freedom (DoFs) that exhibit complex behaviors. Achieving motor intelligence under dynamic conditions necessitates the development of control-oriented reduced-order models (ROMs), which e
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Model-based strategies are a promising solution to the grand challenge of equipping continuum soft robots with motor intelligence. However, finite-dimensional models of these systems are inherently inaccurate, thus posing pressing robustness concerns. Moreover, the actuation spac
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Input Decoupling of Lagrangian Systems via Coordinate Transformation
General Characterization and its Application to Soft Robotics
Suitable representations of dynamical systems can simplify their analysis and control. On this line of thought, this article aims to answer the following question: Can a transformation of the generalized coordinates under which the actuators directly perform work on a subset of t
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Controlled execution of dynamic motions in quadrupedal robots, especially those with articulated soft bodies, presents a unique set of challenges that traditional methods struggle to address efficiently. In this study, we tackle these issues by relying on a simple yet effective t
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The compliant nature of soft robots is appealing to a wide range of applications. However, this compliant property also poses several control challenges, e.g., how to deal with infinite degrees of freedom and highly nonlinear behaviors. This paper proposes a hybrid controller for
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Shared Awareness Across Domain-Specific Artificial Intelligence
An Alternative to Domain-General Intelligence and Artificial Consciousness
Creating artificial general intelligence is the solution most often in the spotlight. It is also linked with the possibility—or fear—of machines gaining consciousness. Alternatively, developing domain-specific artificial intelligence is more reliable, energy-efficient, and ethica
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Formulating the dynamics of continuously deformable objects and other mechanical systems analytically from first principles is an exceedingly challenging task, often impractical in real-world scenarios. What makes this challenge even harder to solve is that, usually, the object h
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Integrating Brain-Machine Interfaces into non-clinical applications like robot motion control remains difficult - despite remarkable advancements in clinical settings. Specifically, EEG-based motor imagery systems are still error-prone, posing safety risks when rigid robots opera
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