Zobrazeno 1 - 10
of 228
pro vyhledávání: '"Righetti, Ludovic"'
The recent promises of Model Predictive Control in robotics have motivated the development of tailored second-order methods to solve optimal control problems efficiently. While those methods benefit from strong convergence properties, tailored effici
Externí odkaz:
http://arxiv.org/abs/2409.18327
Parkour poses a significant challenge for legged robots, requiring navigation through complex environments with agility and precision based on limited sensory inputs. In this work, we introduce a novel method for training end-to-end visual policies,
Externí odkaz:
http://arxiv.org/abs/2409.13678
Autor:
Ortiz-Haro, Joaquim, Hönig, Wolfgang, Hartmann, Valentin N., Toussaint, Marc, Righetti, Ludovic
Rapidly-exploring Random Trees (RRT) and its variations have emerged as a robust and efficient tool for finding collision-free paths in robotic systems. However, adding dynamic constraints makes the motion planning problem significantly harder, as it
Externí odkaz:
http://arxiv.org/abs/2403.10745
Autor:
Dhédin, Victor, Ravi, Adithya Kumar Chinnakkonda, Jordana, Armand, Zhu, Huaijiang, Meduri, Avadesh, Righetti, Ludovic, Schölkopf, Bernhard, Khadiv, Majid
Legged robots have become capable of performing highly dynamic maneuvers in the past few years. However, agile locomotion in highly constrained environments such as stepping stones is still a challenge. In this paper, we propose a combination of mode
Externí odkaz:
http://arxiv.org/abs/2403.03639
Trajectory optimization under uncertainties is a challenging problem for robots in contact with the environment. Such uncertainties are inevitable due to estimation errors, control imperfections, and model mismatches between planning models used for
Externí odkaz:
http://arxiv.org/abs/2309.04469
In robotics, designing robust algorithms in the face of estimation uncertainty is a challenging task. Indeed, controllers often do not consider the estimation uncertainty and only rely on the most likely estimated state. Consequently, sudden changes
Externí odkaz:
http://arxiv.org/abs/2305.11573
Autor:
Pfeiffer, Kai, Jia, Yuze, Yin, Mingsheng, Veldanda, Akshaj Kumar, Hu, Yaqi, Trivedi, Amee, Zhang, Jeff, Garg, Siddharth, Erkip, Elza, Rangan, Sundeep, Righetti, Ludovic
In this paper, we study a navigation problem where a mobile robot needs to locate a mmWave wireless signal. Using the directionality properties of the signal, we propose an estimation and path planning algorithm that can efficiently navigate in clutt
Externí odkaz:
http://arxiv.org/abs/2303.03739
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
Publikováno v:
21st International Conference on Ubiquitous Robots (UR) 2024
In legged logomotion, online trajectory optimization techniques generally depend on heuristic-based contact planners in order to have low computation times and achieve high replanning frequencies. In this work, we propose ContactNet, a fast acyclic c
Externí odkaz:
http://arxiv.org/abs/2209.15566
Model predictive control is a powerful tool to generate complex motions for robots. However, it often requires solving non-convex problems online to produce rich behaviors, which is computationally expensive and not always practical in real time. Add
Externí odkaz:
http://arxiv.org/abs/2209.09451