Zobrazeno 1 - 10
of 64
pro vyhledávání: '"Stueckler, Joerg"'
Object-centric scene decompositions are important representations for downstream tasks in fields such as computer vision and robotics. The recently proposed Slot Attention module, already leveraged by several derivative works for image segmentation a
Externí odkaz:
http://arxiv.org/abs/2407.04170
Physics-based understanding of object interactions from sensory observations is an essential capability in augmented reality and robotics. It enables to capture the properties of a scene for simulation and control. In this paper, we propose a novel a
Externí odkaz:
http://arxiv.org/abs/2309.15703
Autor:
Li, Haolong, Stueckler, Joerg
Wheeled mobile robots need the ability to estimate their motion and the effect of their control actions for navigation planning. In this paper, we present ST-VIO, a novel approach which tightly fuses a single-track dynamics model for wheeled ground v
Externí odkaz:
http://arxiv.org/abs/2309.11148
In autonomous navigation settings, several quantities can be subject to variations. Terrain properties such as friction coefficients may vary over time depending on the location of the robot. Also, the dynamics of the robot may change due to, e.g., d
Externí odkaz:
http://arxiv.org/abs/2307.09206
Autor:
Achterhold, Jan, Tobuschat, Philip, Ma, Hao, Buechler, Dieter, Muehlebach, Michael, Stueckler, Joerg
In this paper, we present a method for table tennis ball trajectory filtering and prediction. Our gray-box approach builds on a physical model. At the same time, we use data to learn parameters of the dynamics model, of an extended Kalman filter, and
Externí odkaz:
http://arxiv.org/abs/2305.15189
Intelligent robots require object-level scene understanding to reason about possible tasks and interactions with the environment. Moreover, many perception tasks such as scene reconstruction, image retrieval, or place recognition can benefit from rea
Externí odkaz:
http://arxiv.org/abs/2305.02398
Visual reconstruction of fast non-rigid object deformations over time is a challenge for conventional frame-based cameras. In this paper, we propose a novel approach for reconstructing such deformations using measurements from event-based cameras. Un
Externí odkaz:
http://arxiv.org/abs/2210.06270
Autor:
Dhédin, Victor, Li, Haolong, Khorshidi, Shahram, Mack, Lukas, Ravi, Adithya Kumar Chinnakkonda, Meduri, Avadesh, Shah, Paarth, Grimminger, Felix, Righetti, Ludovic, Khadiv, Majid, Stueckler, Joerg
Implementing dynamic locomotion behaviors on legged robots requires a high-quality state estimation module. Especially when the motion includes flight phases, state-of-the-art approaches fail to produce reliable estimation of the robot posture, in pa
Externí odkaz:
http://arxiv.org/abs/2210.02127
Problems which require both long-horizon planning and continuous control capabilities pose significant challenges to existing reinforcement learning agents. In this paper we introduce a novel hierarchical reinforcement learning agent which links temp
Externí odkaz:
http://arxiv.org/abs/2207.05018
Autor:
Li, Haolong, Stueckler, Joerg
Visual-inertial odometry (VIO) is an important technology for autonomous robots with power and payload constraints. In this paper, we propose a novel approach for VIO with stereo cameras which integrates and calibrates the velocity-control based kine
Externí odkaz:
http://arxiv.org/abs/2204.06776