Zobrazeno 1 - 10
of 74
pro vyhledávání: '"Häufle, Daniel"'
Human hand and head movements are the most pervasive input modalities in extended reality (XR) and are significant for a wide range of applications. However, prior works on hand and head modelling in XR only explored a single modality or focused on s
Externí odkaz:
http://arxiv.org/abs/2410.16430
We present HOIMotion - a novel approach for human motion forecasting during human-object interactions that integrates information about past body poses and egocentric 3D object bounding boxes. Human motion forecasting is important in many augmented r
Externí odkaz:
http://arxiv.org/abs/2407.02633
We present GazeMotion, a novel method for human motion forecasting that combines information on past human poses with human eye gaze. Inspired by evidence from behavioural sciences showing that human eye and body movements are closely coordinated, Ga
Externí odkaz:
http://arxiv.org/abs/2403.09885
Autor:
Charaja, Jhon, Wochner, Isabell, Schumacher, Pierre, Ilg, Winfried, Giese, Martin, Maufroy, Christophe, Bulling, Andreas, Schmitt, Syn, Haeufle, Daniel F. B.
The mimicking of human-like arm movement characteristics involves the consideration of three factors during control policy synthesis: (a) chosen task requirements, (b) inclusion of noise during movement execution and (c) chosen optimality principles.
Externí odkaz:
http://arxiv.org/abs/2402.13949
Autor:
Schumacher, Pierre, Krause, Lorenz, Schneider, Jan, Büchler, Dieter, Martius, Georg, Haeufle, Daniel
Recent studies have demonstrated the immense potential of exploiting muscle actuator morphology for natural and robust movement -- in simulation. A validation on real robotic hardware is yet missing. In this study, we emulate muscle actuator properti
Externí odkaz:
http://arxiv.org/abs/2402.05371
Autor:
Schneider, Jan, Schumacher, Pierre, Guist, Simon, Chen, Le, Häufle, Daniel, Schölkopf, Bernhard, Büchler, Dieter
Policy gradient methods hold great potential for solving complex continuous control tasks. Still, their training efficiency can be improved by exploiting structure within the optimization problem. Recent work indicates that supervised learning can be
Externí odkaz:
http://arxiv.org/abs/2401.06604
Reinforcement learning~(RL) is a versatile framework for learning to solve complex real-world tasks. However, influences on the learning performance of RL algorithms are often poorly understood in practice. We discuss different analysis techniques an
Externí odkaz:
http://arxiv.org/abs/2309.06921
Autor:
Schumacher, Pierre, Geijtenbeek, Thomas, Caggiano, Vittorio, Kumar, Vikash, Schmitt, Syn, Martius, Georg, Haeufle, Daniel F. B.
Humans excel at robust bipedal walking in complex natural environments. In each step, they adequately tune the interaction of biomechanical muscle dynamics and neuronal signals to be robust against uncertainties in ground conditions. However, it is s
Externí odkaz:
http://arxiv.org/abs/2309.02976
Animals run robustly in diverse terrain. This locomotion robustness is puzzling because axon conduction velocity is limited to a few ten meters per second. If reflex loops deliver sensory information with significant delays, one would expect a destab
Externí odkaz:
http://arxiv.org/abs/2212.00475
Autor:
Wochner, Isabell, Schumacher, Pierre, Martius, Georg, Büchler, Dieter, Schmitt, Syn, Haeufle, Daniel F. B.
Humans are able to outperform robots in terms of robustness, versatility, and learning of new tasks in a wide variety of movements. We hypothesize that highly nonlinear muscle dynamics play a large role in providing inherent stability, which is favor
Externí odkaz:
http://arxiv.org/abs/2207.03952