45 citations · 46 across the 10 of their papers we have counts for
10 papers
Two2Four: Generative Quadruped Puppeteering from Human Motion
Fatemeh Zargarbashi, Zehong Qiu, Dhruv Agrawal +4
Realistic animal motion for virtual production is typically obtained either through motion capture of highly trained performers who accurately mimic animal behavior, or by retarget…
VQ-Style: Disentangling Style and Content in Motion with Residual Quantized Representations
Fatemeh Zargarbashi, Dhruv Agrawal, Jakob Buhmann +3
Human motion data is inherently rich and complex, containing both semantic content and subtle stylistic features that are challenging to model. We propose a novel method for effect…
Teaching Robots Like Dogs: Learning Agile Navigation from Luring, Gesture, and Speech
Taerim Yoon, Dongho Kang, Jin Cheng +5
In this work, we aim to enable legged robots to learn how to interpret human social cues and produce appropriate behaviors through physical human guidance. However, learning throug…
Whole-Body Inverse Dynamics MPC for Legged Loco-Manipulation
Lukas Molnar, Jin Cheng, Gabriele Fadini +3
Loco-manipulation demands coordinated whole-body motion to manipulate objects effectively while maintaining locomotion stability, presenting significant challenges for both plannin…
Switch-JustDance: Benchmarking Whole Body Motion Tracking Controllers Using a Commercial Console Game
Jeonghwan Kim, Wontaek Kim, Yidan Lu +9
Recent advances in whole-body robot control have enabled humanoid and legged robots to perform increasingly agile and coordinated motions. However, standardized benchmarks for eval…
Learning to Walk in Costume: Adversarial Motion Priors for Aesthetically Constrained Humanoids
Arturo Flores Alvarez, Fatemeh Zargarbashi, Havel Liu +7
We present a Reinforcement Learning (RL)-based locomotion system for Cosmo, a custom-built humanoid robot designed for entertainment applications. Unlike traditional humanoids, ent…
Walk Like Dogs: Learning Steerable Imitation Controllers for Legged Robots from Unlabeled Motion Data
Dongho Kang, Jin Cheng, Fatemeh Zargarbashi +3
We present an imitation learning framework that extracts distinctive legged locomotion behaviors and transitions between them from unlabeled real-world motion data. By automaticall…
MetaLoco: Universal Quadrupedal Locomotion with Meta-Reinforcement Learning and Motion Imitation
Fatemeh Zargarbashi, Fabrizio Di Giuro, Jin Cheng +3
This work presents a meta-reinforcement learning approach to develop a universal locomotion control policy capable of zero-shot generalization across diverse quadrupedal platforms.…
RobotKeyframing: Learning Locomotion with High-Level Objectives via Mixture of Dense and Sparse Rewards
Fatemeh Zargarbashi, Jin Cheng, Dongho Kang +2
This paper presents a novel learning-based control framework that uses keyframing to incorporate high-level objectives in natural locomotion for legged robots. These high-level obj…
RL + Model-based Control: Using On-demand Optimal Control to Learn Versatile Legged Locomotion
Dongho Kang, Jin Cheng, Miguel Zamora +2
This paper presents a control framework that combines model-based optimal control and reinforcement learning (RL) to achieve versatile and robust legged locomotion. Our approach en…