Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Meduri, Avadesh"'
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
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:
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
This paper presents an efficient approach to object manipulation planning using Monte Carlo Tree Search (MCTS) to find contact sequences and an efficient ADMM-based trajectory optimization algorithm to evaluate the dynamic feasibility of candidate co
Externí odkaz:
http://arxiv.org/abs/2206.09023
Autor:
Meduri, Avadesh, Shah, Paarth, Viereck, Julian, Khadiv, Majid, Havoutis, Ioannis, Righetti, Ludovic
Online planning of whole-body motions for legged robots is challenging due to the inherent nonlinearity in the robot dynamics. In this work, we propose a nonlinear MPC framework, the BiConMP which can generate whole body trajectories online by effici
Externí odkaz:
http://arxiv.org/abs/2201.07601
Optimal control is a successful approach to generate motions for complex robots, in particular for legged locomotion. However, these techniques are often too slow to run in real time for model predictive control or one needs to drastically simplify t
Externí odkaz:
http://arxiv.org/abs/2201.04090
Autor:
Shah, Paarth, Meduri, Avadesh, Merkt, Wolfgang, Khadiv, Majid, Havoutis, Ioannis, Righetti, Ludovic
In this paper we explore the use of block coordinate descent (BCD) to optimize the centroidal momentum dynamics for dynamically consistent multi-contact behaviors. The centroidal dynamics have recently received a large amount of attention in order to
Externí odkaz:
http://arxiv.org/abs/2108.01797
Reactive stepping and push recovery for biped robots is often restricted to flat terrains because of the difficulty in computing capture regions for nonlinear dynamic models. In this paper, we address this limitation by using reinforcement learning t
Externí odkaz:
http://arxiv.org/abs/2010.14834