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…