CoRL 2026
ADAPT: Agile Diffusion Action Priors for Robust and Steerable Online Text-Driven Humanoid Control
We present ADAPT, an end-to-end framework for interactive, text-conditioned humanoid whole-body control.
Paper-Conference
CoRL 2026
EgoHTR: Egocentric 4D Demonstrations of Human Terrain Traversal
Deploying humanoid robots in unstructured terrain remains an open problem.
CoRL 2026
Morphology-Conditioned World Model for Cross-Embodiment Quadrupedal Locomotion
World models promise a paradigm shift in robotics, where an agent learns the physics of its environment once and then acquires behaviors efficiently.
CDC 2026
Model-Based Reinforcement Learning for Control under Time-Varying Dynamics
Learning-based control methods typically assume stationary system dynamics, an assumption often violated in real-world systems due to drift, wear, or changing operating conditions.
CVPR 2026
NIL: No-data Imitation Learning
We propose a novel imitation learning framework that combines video-embedding similarity and image-based similarity to learn 3D motor skills from 2D-generated videos, demonstrating strong performance across various morphologies and tasks.
CoRL 2025
Constrained Style Learning from Imperfect Demonstrations under Task Optimality
We propose a constrained style learning approach that balances task performance and stylistic imitation.
CoRL 2025
Motion Priors Reimagined: Adapting Flat-Terrain Skills for Complex Quadruped Mobility
We propose a hierarchical reinforcement learning method that extends flat-terrain skills for complex terrain navigation.
arXiv 2025
Symmetry-Guided Memory Augmentation for Efficient Locomotion Learning
We propose a memory-enhanced augmentation method for reinforcement learning that extends OOD generalization with context-awareness.
ICRA 2025
DFM: Deep Fourier Mimic for Expressive Dance Motion Learning
We propose an efficient motion representation method with frequency-domain parameterizations that enable expressive policy learning.
NeurIPS 2024
Dataset and Lessons Learned from the 2024 SaTML LLM Capture-the-Flag Competition
Large language model systems face important security risks from maliciously crafted messages that aim to overwrite the system's original instructions or leak private data.
ICRA 2024
Learning Diverse Skills for Local Navigation under Multi-constraint Optimality
We propose a constraint grouping method for diversity optimization while maintaining near-optimality.
ICLR 2023Spotlight
FLD: Fourier Latent Dynamics for Structured Motion Representation and Learning
We propose a self-supervised, structured representation and generation method that extracts spatial-temporal relationships in periodic or quasi-periodic motions.
CoRL 2022Best Paper Award Finalist
Learning Agile Skills via Adversarial Imitation of Rough Partial Demonstrations
We propose a generative adversarial method for inferring reward functions from partial and potentially physically incompatible demonstrations for successful skill acquisition where reference or expert demonstrations are not easily accessible.