Zobrazeno 1 - 10
of 290
pro vyhledávání: '"Lee, Dongheui"'
This paper introduces a new approach to enhance the robustness of humanoid walking under strong perturbations, such as substantial pushes. Effective recovery from external disturbances requires bipedal robots to dynamically adjust their stepping stra
Externí odkaz:
http://arxiv.org/abs/2411.01000
Autor:
Mascaro, Esteve Valls, Lee, Dongheui
As humanoid robots transition from labs to real-world environments, it is essential to democratize robot control for non-expert users. Recent human-robot imitation algorithms focus on following a reference human motion with high precision, but they a
Externí odkaz:
http://arxiv.org/abs/2409.10308
This paper addresses the critical need for refining robot motions that, despite achieving a high visual similarity through human-to-humanoid retargeting methods, fall short of practical execution in the physical realm. Existing techniques in the grap
Externí odkaz:
http://arxiv.org/abs/2405.08726
This article introduces a framework for complex human-robot collaboration tasks, such as the co-manufacturing of furniture. For these tasks, it is essential to encode tasks from human demonstration and reproduce these skills in a compliant and safe m
Externí odkaz:
http://arxiv.org/abs/2403.12720
Robot Interaction Behavior Generation based on Social Motion Forecasting for Human-Robot Interaction
Integrating robots into populated environments is a complex challenge that requires an understanding of human social dynamics. In this work, we propose to model social motion forecasting in a shared human-robot representation space, which facilitates
Externí odkaz:
http://arxiv.org/abs/2402.04768
Robots are becoming increasingly integrated into our lives, assisting us in various tasks. To ensure effective collaboration between humans and robots, it is essential that they understand our intentions and anticipate our actions. In this paper, we
Externí odkaz:
http://arxiv.org/abs/2309.16524
Recently, several approaches have attempted to combine motion generation and control in one loop to equip robots with reactive behaviors, that cannot be achieved with traditional time-indexed tracking controllers. These approaches however mainly focu
Externí odkaz:
http://arxiv.org/abs/2309.15624
This paper introduces a novel deep-learning approach for human-to-robot motion retargeting, enabling robots to mimic human poses accurately. Contrary to prior deep-learning-based works, our method does not require paired human-to-robot data, which fa
Externí odkaz:
http://arxiv.org/abs/2309.05310
The synthesis of human motion has traditionally been addressed through task-dependent models that focus on specific challenges, such as predicting future motions or filling in intermediate poses conditioned on known key-poses. In this paper, we prese
Externí odkaz:
http://arxiv.org/abs/2308.07301
In this paper, we present a novel learning-based shared control framework. This framework deploys first-order Dynamical Systems (DS) as motion generators providing the desired reference motion, and a Variable Stiffness Dynamical Systems (VSDS) \cite{
Externí odkaz:
http://arxiv.org/abs/2307.09887