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20192026
most citedImproved Learning of Robot Manipulation Tasks via Tactile Intrinsic Motivation

27 citations · 28 across the 8 of their papers we have counts for

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14 papers · 1 filter

cs.RO2026

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…

cs.RO2026

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…

cs.RO2025

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…

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.RO2025

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…

cs.RO2025

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…