Zobrazeno 1 - 10
of 393
pro vyhledávání: '"Zamora, Miguel"'
Autor:
Obrist, Jan, Zamora, Miguel, Zheng, Hehui, Hinchet, Ronan, Ozdemir, Firat, Zarate, Juan, Katzschmann, Robert K., Coros, Stelian
Data-driven methods have shown great potential in solving challenging manipulation tasks, however, their application in the domain of deformable objects has been constrained, in part, by the lack of data. To address this, we propose PokeFlex, a datas
Externí odkaz:
http://arxiv.org/abs/2410.07688
Autor:
Obrist, Jan, Zamora, Miguel, Zheng, Hehui, Zarate, Juan, Katzschmann, Robert K., Coros, Stelian
Advancing robotic manipulation of deformable objects can enable automation of repetitive tasks across multiple industries, from food processing to textiles and healthcare. Yet robots struggle with the high dimensionality of deformable objects and the
Externí odkaz:
http://arxiv.org/abs/2409.17124
Autor:
Martinez-Murcia, Francisco Jesus, Ortiz, Andrés, Górriz, Juan Manuel, Ramírez, Javier, Lopez-Perez, Pedro Javier, López-Zamora, Miguel, Luque, Juan Luis
Publikováno v:
INT J NEURAL SYST 30 (7), 2020, 2050037
The Temporal Sampling Framework (TSF) theorizes that the characteristic phonological difficulties of dyslexia are caused by an atypical oscillatory sampling at one or more temporal rates. The LEEDUCA study conducted a series of Electroencephalography
Externí odkaz:
http://arxiv.org/abs/2311.13876
Precise reconstruction and manipulation of the crumpled cloths is challenging due to the high dimensionality of cloth models, as well as the limited observation at self-occluded regions. We leverage the recent progress in the field of single-view hum
Externí odkaz:
http://arxiv.org/abs/2308.04670
Unlike human beings that can employ the entire surface of their limbs as a means to establish contact with their environment, robots are typically programmed to interact with their environments via their end-effectors, in a collision-free fashion, to
Externí odkaz:
http://arxiv.org/abs/2308.04323
This paper presents a control framework that combines model-based optimal control and reinforcement learning (RL) to achieve versatile and robust legged locomotion. Our approach enhances the RL training process by incorporating on-demand reference mo
Externí odkaz:
http://arxiv.org/abs/2305.17842
Autor:
Sukhija, Bhavya, Köhler, Nathanael, Zamora, Miguel, Zimmermann, Simon, Curi, Sebastian, Krause, Andreas, Coros, Stelian
Trajectory optimization methods have achieved an exceptional level of performance on real-world robots in recent years. These methods heavily rely on accurate analytical models of the dynamics, yet some aspects of the physical world can only be captu
Externí odkaz:
http://arxiv.org/abs/2204.04558
Publikováno v:
IEEE Robotics and Automation Letters, Volume: 7, Issue: 3, p. 7912 - 7919, July 2022
A variety of control tasks such as inverse kinematics (IK), trajectory optimization (TO), and model predictive control (MPC) are commonly formulated as energy minimization problems. Numerical solutions to such problems are well-established. However,
Externí odkaz:
http://arxiv.org/abs/2203.03432
Advancing Free-Form Fabrication: Industrial Robots' Role in Additive Manufacturing of Thermoplastics
Publikováno v:
In Procedia Computer Science 2024 232:3131-3140
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