Agon Serifi

I am an Associate Research Scientist with the Disney Research Robotics team. My work focuses on deep reinforcement learning and human motion to develop control methods that enable robots to generalize and exhibit rich, diverse behaviors. This includes motion imitation, generation, and the reproduction of lifelike behavior. I received my PhD in Computer Science (2025) from the Computer Graphics Laboratory (CGL) at ETH Zurich under Prof. Markus Gross and Dr. Moritz Bächer, in collaboration with Disney Research. I also hold a Bachelor's (2019) and a Master's (2021) degree in Computer Science from ETH Zurich.

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CoCo-InEKF: State Estimation with Learned Contact Covariances in Dynamic, Contact-Rich Scenarios

Michael Baumgartner, David Müller, Agon Serifi, Ruben Grandia, Espen Knoop, Markus Gross, Moritz Bächer

RSS 2026

paper | video | ArXiv

CoCo-InEKF is a differentiable Invariant Extended Kalman Filter that replaces binary contact states with continuous contact covariances learned end-to-end. This improves velocity estimation on a bipedal robot and enables robust, contact-rich behaviors like dancing without heuristic contact labels.

ReActor: Reinforcement Learning for Physics-Aware Motion Retargeting

David Müller, Agon Serifi, Sammy Christen, Ruben Grandia, Espen Knoop, Moritz Bächer

SIGGRAPH 2026

paper | ArXiv

ReActor is a bilevel optimization framework that jointly adapts human reference motion to a robot's morphology while training a tracking policy with reinforcement learning. Using only sparse semantic correspondences, it eliminates manual retargeting and produces physically plausible motion even on morphologies far from human, including a quadruped.

Kamino: GPU-based Massively Parallel Simulation of Multi-Body Systems with Challenging Topologies

Vassilios Tsounis, Guirec Maloisel, Christian Schumacher, Ruben Grandia, Agon Serifi, David Müller, Chris Amevor, Tobias Widmer, Moritz Bächer

arXiv 2026

project page | paper | code | ArXiv

Kamino is a GPU-based physics solver for massively parallel simulation of heterogeneous, highly-coupled mechanical systems, natively supporting complex topologies with kinematic loops. Built on NVIDIA Warp and integrated into Newton, it enables batched simulation of structurally diverse robots on a single GPU for reinforcement learning.

Olaf: Bringing an Animated Character to Life in the Physical World

David Müller*, Espen Knoop*, Dario Mylonopoulos, Agon Serifi, Michael A. Hopkins, Ruben Grandia, Moritz Bächer

IEEE Robotics and Automation Letters (RA-L) 2026

paper | video | IEEE

In this work, we bring Olaf, an animated character, to life in the physical world, relying on reinforcement learning guided by animation references for control.

Robot Crash Course: Learning Soft and Stylized Falling

Pascal Strauch, David Müller, Sammy Christen, Agon Serifi, Ruben Grandia, Espen Knoop, Moritz Bächer

ICRA 2026

paper | video | ArXiv

We learn controlled, soft falls for bipedal robots that minimize physical damage while achieving desired end poses, using a robot-agnostic reward during reinforcement learning. The method supports arbitrary end-pose specification at inference time and demonstrates robust falling in both simulation and the real world.

AMOR: Adaptive Character Control through Multi-Objective Reinforcement Learning

Lucas N. Alegre*, Agon Serifi*, Ruben Grandia, David Müller, Espen Knoop, Moritz Bächer

SIGGRAPH 2025

project page | paper | video | ACM

AMOR enables adaptive character control by training a single multi-objective reinforcement learning policy conditioned on reward weights and motion context. This allows post-training tuning of reward trade-offs, zero-shot adaptation, and sim-to-real transfer of dynamic motions across a broad range of tasks.

Autonomous Human-Robot Interaction via Operator Imitation

Sammy Christen, David Müller, Agon Serifi, Ruben Grandia, Georg Wiedebach, Michael A. Hopkins, Espen Knoop, Moritz Bächer

IROS 2025

project page | paper | video | IEEE

We autonomously control expressive human-robot interactions by training a diffusion-based imitation model on real operator data. Conditioned on human pose, it predicts continuous and discrete control inputs, producing robust, lifelike interactions and diverse moods that resemble expert operators.

Robot Motion Diffusion Model: Motion Generation for Robotic Characters

Agon Serifi, Ruben Grandia, Espen Knoop, Markus Gross, Moritz Bächer

SIGGRAPH Asia 2024

project page | paper | video | ACM

We align text-conditioned kinematic motion generators with the capabilities of robotic characters by combining motion generation with physics-based control. A learned critic acts as a differentiable reward surrogate for the non-differentiable control task, aligning the generator with the character's physical capabilities.

Spline-Based Transformers

Prashanth Chandran*, Agon Serifi*, Markus Gross, Moritz Bächer

ECCV 2024 (Oral)

project page | paper | supp | video | Springer

Spline-Based Transformers are a new class of Transformers that eliminate positional encoding by embedding an input sequence as a smooth trajectory in latent space. Leveraging the continuity of sequential data, they outperform traditional transformers in efficiency and accuracy.

VMP: Versatile Motion Priors for Robustly Tracking Motion on Physical Characters

Agon Serifi, Ruben Grandia, Espen Knoop, Markus Gross, Moritz Bächer

SIGGRAPH/Eurographics Symposium on Computer Animation (SCA) 2024

project page | paper | video | ACM

VMP is a two-stage framework that transfers expressive, dynamic motion onto robotic characters by first learning a latent motion prior from human motion capture. This prior then trains a reinforcement learning tracking controller, enabling lifelike, dynamic motions such as dancing.

Transformer-Based Neural Augmentation of Robot Simulation Representations

Agon Serifi, Espen Knoop, Christian Schumacher, Naveen Kumar, Markus Gross, Moritz Bächer

IEEE Robotics and Automation Letters (RA-L), presented at IROS 2023

project page | paper | video | IEEE

This paper introduces a modular approach for neural augmentation of simulation states, improving robot simulation accuracy by targeting individual building blocks such as actuators and rigid bodies separately.

PhD Thesis

Lifelike Motions for Robotic Characters

Agon Serifi

Doctoral Thesis, ETH Zurich 2025 · Advisors: Markus Gross, Moritz Bächer

page | pdf | DOI

My dissertation on bringing lifelike, dynamic motion to robotic characters, combining physically accurate simulation, learned latent motion representations, and reinforcement-learning control to close the sim-to-real gap and transfer expressive motions such as dancing to a real humanoid.