37 citations · 124 across the 22 of their papers we have counts for
25 papers
Walk Like This: Stylized Locomotion for Physics-Based Characters through Example-Driven Diffusion
Joshua Aurand, Agon Serifi, Sammy Christen +4
We present a real-time framework for expressive legged locomotion that enables independent control over velocity and style. Unlike existing data-driven controllers, which typically…
BFMTrack: Latent Sequence Optimization for Physics-Based Motion Tracking with Behavioral Foundation Models
Thomas Rupf, Agon Serifi, David Müller +4
Behavioral Foundation Models (BFMs) offer a promising path toward universal physics-based character control by organizing a rich repertoire of physically plausible behaviors into a…
ReActor: Reinforcement Learning for Physics-Aware Motion Retargeting
David Müller, Agon Serifi, Sammy Christen +3
Retargeting human kinematic reference motion onto a robot's morphology remains a formidable challenge. Existing methods often produce physical inconsistencies, such as foot sliding…
Robot Crash Course: Learning Soft and Stylized Falling
Pascal Strauch, David Müller, Sammy Christen +4
Despite recent advances in robust locomotion, bipedal robots operating in the real world remain at risk of falling. While most research focuses on preventing such events, we instea…
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…
RobustDexGrasp: Robust Dexterous Grasping of General Objects
Hui Zhang, Zijian Wu, Linyi Huang +2
The ability to robustly grasp a variety of objects is essential for dexterous robots. In this paper, we present a framework for zero-shot dynamic dexterous grasping using single-vi…
Modeling Dynamic Hand-Object Interactions with Applications to Human-Robot Handovers
Sammy Christen
Humans frequently grasp, manipulate, and move objects. Interactive systems assist humans in these tasks, enabling applications in Embodied AI, human-robot interaction, and virtual…
FunGrasp: Functional Grasping for Diverse Dexterous Hands
Linyi Huang, Hui Zhang, Zijian Wu +2
Functional grasping is essential for humans to perform specific tasks, such as grasping scissors by the finger holes to cut materials or by the blade to safely hand them over. Enab…
EgoHDM: An Online Egocentric-Inertial Human Motion Capture, Localization, and Dense Mapping System
Bonan Liu, Handi Yin, Manuel Kaufmann +4
We present EgoHDM, an online egocentric-inertial human motion capture (mocap), localization, and dense mapping system. Our system uses 6 inertial measurement units (IMUs) and a com…
Omnigrasp: Grasping Diverse Objects with Simulated Humanoids
Zhengyi Luo, Jinkun Cao, Sammy Christen +3
We present a method for controlling a simulated humanoid to grasp an object and move it to follow an object's trajectory. Due to the challenges in controlling a humanoid with dexte…
RILe: Reinforced Imitation Learning
Mert Albaba, Sammy Christen, Thomas Langarek +3
Acquiring complex behaviors is essential for artificially intelligent agents, yet learning these behaviors in high-dimensional settings poses a significant challenge due to the vas…
GraspXL: Generating Grasping Motions for Diverse Objects at Scale
Hui Zhang, Sammy Christen, Zicong Fan +2
Human hands possess the dexterity to interact with diverse objects such as grasping specific parts of the objects and/or approaching them from desired directions. More importantly,…
DiffH2O: Diffusion-Based Synthesis of Hand-Object Interactions from Textual Descriptions
Sammy Christen, Shreyas Hampali, Fadime Sener +5
Generating natural hand-object interactions in 3D is challenging as the resulting hand and object motions are expected to be physically plausible and semantically meaningful. Furth…
SynH2R: Synthesizing Hand-Object Motions for Learning Human-to-Robot Handovers
Sammy Christen, Lan Feng, Wei Yang +3
Vision-based human-to-robot handover is an important and challenging task in human-robot interaction. Recent work has attempted to train robot policies by interacting with dynamic…
Physically Plausible Full-Body Hand-Object Interaction Synthesis
Jona Braun, Sammy Christen, Muhammed Kocabas +2
We propose a physics-based method for synthesizing dexterous hand-object interactions in a full-body setting. While recent advancements have addressed specific facets of human-obje…
ArtiGrasp: Physically Plausible Synthesis of Bi-Manual Dexterous Grasping and Articulation
Hui Zhang, Sammy Christen, Zicong Fan +4
We present ArtiGrasp, a novel method to synthesize bi-manual hand-object interactions that include grasping and articulation. This task is challenging due to the diversity of the g…
Learning Human-to-Robot Handovers from Point Clouds
Sammy Christen, Wei Yang, Claudia Pérez-D'Arpino +3
We propose the first framework to learn control policies for vision-based human-to-robot handovers, a critical task for human-robot interaction. While research in Embodied AI has m…
MARLUI: Multi-Agent Reinforcement Learning for Adaptive UIs
Thomas Langerak, Sammy Christen, Mert Albaba +2
Adaptive user interfaces (UIs) automatically change an interface to better support users' tasks. Recently, machine learning techniques have enabled the transition to more powerful…
TempCLR: Reconstructing Hands via Time-Coherent Contrastive Learning
Andrea Ziani, Zicong Fan, Muhammed Kocabas +2
We introduce TempCLR, a new time-coherent contrastive learning approach for the structured regression task of 3D hand reconstruction. Unlike previous time-contrastive methods for h…
SFP: State-free Priors for Exploration in Off-Policy Reinforcement Learning
Marco Bagatella, Sammy Christen, Otmar Hilliges
Efficient exploration is a crucial challenge in deep reinforcement learning. Several methods, such as behavioral priors, are able to leverage offline data in order to efficiently a…
D-Grasp: Physically Plausible Dynamic Grasp Synthesis for Hand-Object Interactions
Sammy Christen, Muhammed Kocabas, Emre Aksan +3
We introduce the dynamic grasp synthesis task: given an object with a known 6D pose and a grasp reference, our goal is to generate motions that move the object to a target 6D pose.…
Improved Learning of Robot Manipulation Tasks via Tactile Intrinsic Motivation
Nikola Vulin, Sammy Christen, Stefan Stevsic +1
In this paper we address the challenge of exploration in deep reinforcement learning for robotic manipulation tasks. In sparse goal settings, an agent does not receive any positive…
The Six Hug Commandments: Design and Evaluation of a Human-Sized Hugging Robot with Visual and Haptic Perception
Alexis E. Block, Sammy Christen, Roger Gassert +2
Receiving a hug is one of the best ways to feel socially supported, and the lack of social touch can have severe negative effects on an individual's well-being. Based on previous r…
Learning Functionally Decomposed Hierarchies for Continuous Control Tasks with Path Planning
Sammy Christen, Lukas Jendele, Emre Aksan +1
We present HiDe, a novel hierarchical reinforcement learning architecture that successfully solves long horizon control tasks and generalizes to unseen test scenarios. Functional d…