most citedAMOR: Adaptive Character Control through Multi-Objective Reinforcement Learning

3 citations · 3 across the 6 of their papers we have counts for

collaborators

6 papers

cs.RO2025

Attention-Based Map Encoding for Learning Generalized Legged Locomotion

Junzhe He, Chong Zhang, Fabian Jenelten +3

Dynamic locomotion of legged robots is a critical yet challenging topic in expanding the operational range of mobile robots. It requires precise planning when possible footholds ar…

cs.RO20253 cited

AMOR: Adaptive Character Control through Multi-Objective Reinforcement Learning

Lucas N. Alegre, Agon Serifi, Ruben Grandia +3

Reinforcement learning (RL) has significantly advanced the control of physics-based and robotic characters that track kinematic reference motion. However, methods typically rely on…

cs.RO2025

On Solving the Dynamics of Constrained Rigid Multi-Body Systems with Kinematic Loops

Vassilios Tsounis, Ruben Grandia, Moritz Bächer

This technical report provides an in-depth evaluation of both established and state-of-the-art methods for simulating constrained rigid multi-body systems with hard-contact dynamic…

cs.LG2025

Spline-based Transformers

Prashanth Chandran, Agon Serifi, Markus Gross +1

We introduce Spline-based Transformers, a novel class of Transformer models that eliminate the need for positional encoding. Inspired by workflows using splines in computer animati…

cs.RO2025

Autonomous Human-Robot Interaction via Operator Imitation

Sammy Christen, David Müller, Agon Serifi +5

Teleoperated robotic characters can perform expressive interactions with humans, relying on the operators' experience and social intuition. In this work, we propose to create auton…

cs.GR2019

Data-Driven Physical Face Inversion

Yeara Kozlov, Hongyi Xu, Moritz Bächer +3

Facial animation is one of the most challenging problems in computer graphics, and it is often solved using linear heuristics like blend-shape rigging. More expressive approaches l…