20 papers · 1 filter
Humanoid Mobility via Attention-based Contacts
Junzhe He, Kehan Wen, Chong Zhang +7
Humanoid mobility over complex terrain requires accurate foot placement and coordinated whole-body motion capable of robust adaptation. Reinforcement learning (RL) policies trained…
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…
HOI-Retarget: Contact-Centric Retargeting for Human-Object Interaction
Jihwan Shin, Adrià López Escoriza, Junzhe He +2
Learning from demonstration (LfD) has enabled humanoid robots to acquire diverse whole-body skills, but extending this paradigm to human-object interaction (HOI) is limited by the…
ADAPT: Agile Diffusion Action Priors for Robust and Steerable Online Text-Driven Humanoid Control
Yan Wu, Chenhao Li, Kaifeng Zhao +3
We present ADAPT, an end-to-end framework for interactive, text-conditioned humanoid whole-body control. Unlike dominant text-to-motion pipelines that generate kinematic motions fo…
Learning Loco-Manipulation From SMPC Demonstrations With Sparse Offline-to-Online RL
Martin Schuck, Maks Sorokin, Simone Manni +5
Integrating locomotion and manipulation is essential for robot autonomy, but scaling standard Reinforcement Learning (RL) to complex tasks is severely bottlenecked by the slow, man…
EgoHTR: Egocentric 4D Demonstrations of Human Terrain Traversal
Alex Brandes, Haig Conti Georges Sajelian, Manthan Patel +11
Deploying humanoid robots in unstructured terrain remains an open problem. While classic reinforcement learning struggles with the sheer complexity of real-world interactions, more…
Robots Need More than VLA and World Models
Elis Karcini, Faisal Mehrban, Quang Nguyen +6
Generalist robot intelligence is often framed as a policy-scaling problem: collect more robot demonstrations, train larger Vision-Language-Action (VLA) models, and expect broader g…
Learning Whole-Body Humanoid Locomotion via Motion Generation and Motion Tracking
Zewei Zhang, Kehan Wen, Michael Xu +7
Whole-body humanoid locomotion is challenging due to high-dimensional control, morphological instability, and the need for real-time adaptation to various terrains using onboard pe…
PAINT: Partner-Agnostic Intent-Aware Cooperative Transport with Legged Robots
Zhihao Cao, Tianxu An, Chenhao Li +2
Collaborative transport requires robots to infer partner intent through physical interaction while maintaining stable loco-manipulation. This becomes particularly challenging in co…
Morphology-Conditioned World Model for Cross-Embodiment Quadrupedal Locomotion
Mohamad H. Danesh, Chenhao Li, Amin Abyaneh +5
World models promise a paradigm shift in robotics, where an agent learns the physics of its environment once and then acquires behaviors efficiently. Yet the learned dynamics model…
Scaling Rough Terrain Locomotion with Automatic Curriculum Reinforcement Learning
Ziming Li, Chenhao Li, Marco Hutter
Curriculum learning has demonstrated substantial effectiveness in robot learning. However, it still faces limitations when scaling to complex, wide-ranging task spaces. Such task s…
Multi-Domain Motion Embedding: Expressive Real-Time Mimicry for Legged Robots
Matthias Heyrman, Chenhao Li, Victor Klemm +3
Effective motion representation is crucial for enabling robots to imitate expressive behaviors in real time, yet existing motion controllers often ignore inherent patterns in motio…
Collaborative Loco-Manipulation for Pick-and-Place Tasks with Dynamic Reward Curriculum
Tianxu An, Flavio De Vincenti, Yuntao Ma +2
We present a hierarchical RL pipeline for training one-armed legged robots to perform pick-and-place (P&P) tasks end-to-end -- from approaching the payload to releasing it at a tar…
Constrained Style Learning from Imperfect Demonstrations under Task Optimality
Kehan Wen, Chenhao Li, Junzhe He +1
Learning from demonstration has proven effective in robotics for acquiring natural behaviors, such as stylistic motions and lifelike agility, particularly when explicitly defining…
Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuning
Nikita Rudin, Junzhe He, Joshua Aurand +1
Legged robots are well-suited for navigating terrains inaccessible to wheeled robots, making them ideal for applications in search and rescue or space exploration. However, current…
Motion Priors Reimagined: Adapting Flat-Terrain Skills for Complex Quadruped Mobility
Zewei Zhang, Chenhao Li, Takahiro Miki +1
Reinforcement learning (RL)-based motion imitation methods trained on demonstration data can effectively learn natural and expressive motions with minimal reward engineering but of…
Uncertainty-Aware Robotic World Model Makes Offline Model-Based Reinforcement Learning Work on Real Robots
Chenhao Li, Andreas Krause, Marco Hutter
Reinforcement Learning (RL) has achieved impressive results in robotics, yet high-performing pipelines remain highly task-specific, with little reuse of prior data. Offline Model-b…
Learning Quiet Walking for a Small Home Robot
Ryo Watanabe, Takahiro Miki, Fan Shi +5
As home robotics gains traction, robots are increasingly integrated into households, offering companionship and assistance. Quadruped robots, particularly those resembling dogs, ha…
DFM: Deep Fourier Mimic for Expressive Dance Motion Learning
Ryo Watanabe, Chenhao Li, Marco Hutter
As entertainment robots gain popularity, the demand for natural and expressive motion, particularly in dancing, continues to rise. Traditionally, dancing motions have been manually…
Robotic World Model: A Neural Network Simulator for Robust Policy Optimization in Robotics
Chenhao Li, Andreas Krause, Marco Hutter
Learning robust and generalizable world models is crucial for enabling efficient and scalable robotic control in real-world environments. In this work, we introduce a novel framewo…