activity
20192025
most citedLearning Whole-body Motor Skills for Humanoids

21 citations · 38 across the 4 of their papers we have counts for

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11 papers · 1 filter

cs.RO2025

Reference Free Platform Adaptive Locomotion for Quadrupedal Robots using a Dynamics Conditioned Policy

David Rytz, Suyoung Choi, Wanming Yu +3

This article presents Platform Adaptive Locomotion (PAL), a unified control method for quadrupedal robots with different morphologies and dynamics. We leverage deep reinforcement l…

cs.RO2021

Rapid Convex Optimization of Centroidal Dynamics using Block Coordinate Descent

Paarth Shah, Avadesh Meduri, Wolfgang Merkt +3

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 d…

cs.RO202114 cited

HapFIC: An Adaptive Force/Position Controller for Safe Environment Interaction in Articulated Systems

Carlo Tiseo, Wolfgang Merkt, Keyhan Kouhkiloui Babarahmati +4

Haptic interaction is essential for the dynamic dexterity of animals, which seamlessly switch from an impedance to an admittance behaviour using the force feedback from their propr…

cs.RO2021

CPG-ACTOR: Reinforcement Learning for Central Pattern Generators

Luigi Campanaro, Siddhant Gangapurwala, Daniele De Martini +2

Central Pattern Generators (CPGs) have several properties desirable for locomotion: they generate smooth trajectories, are robust to perturbations and are simple to implement. Alth…

cs.RO2020

Inverse Dynamics vs. Forward Dynamics in Direct Transcription Formulations for Trajectory Optimization

Henrique Ferrolho, Vladimir Ivan, Wolfgang Merkt +2

Benchmarks of state-of-the-art rigid-body dynamics libraries report better performance solving the inverse dynamics problem than the forward alternative. Those benchmarks encourage…

cs.RO2020

Memory Clustering using Persistent Homology for Multimodality- and Discontinuity-Sensitive Learning of Optimal Control Warm-starts

Wolfgang Merkt, Vladimir Ivan, Traiko Dinev +2

Shooting methods are an efficient approach to solving nonlinear optimal control problems. As they use local optimization, they exhibit favorable convergence when initialized with a…