5 papers · 1 filter
Multimodal Diffusion Forcing for Forceful Manipulation
Zixuan Huang, Huaidian Hou, Dmitry Berenson
Given a dataset of expert trajectories, standard imitation learning approaches typically learn a direct mapping from observations (e.g., RGB images) to actions. However, such metho…
CADRE: Dynamic Catching via Implicit Contact Descriptors and Task-Appropriate Recovery Affordances
Fan Yang, Zixuan Huang, Abhinav Kumar +4
Real-world dexterous manipulation often encounters unexpected errors and disturbances, which can lead to catastrophic failures, such as dropping the manipulated object. To address…
AnoF-Diff: One-Step Diffusion-Based Anomaly Detection for Forceful Tool Use
Yating Lin, Zixuan Huang, Fan Yang +1
Multivariate time-series anomaly detection, which is critical for identifying unexpected events, has been explored in the field of machine learning for several decades. However, di…
Planning-Query-Guided Model Generation for Model-Based Deformable Object Manipulation
Alex LaGrassa, Zixuan Huang, Dmitry Berenson +1
Efficient planning in high-dimensional spaces, such as those involving deformable objects, requires computationally tractable yet sufficiently expressive dynamics models. This pape…
Implicit Contact Diffuser: Sequential Contact Reasoning with Latent Point Cloud Diffusion
Zixuan Huang, Yinong He, Yating Lin +1
Long-horizon contact-rich manipulation has long been a challenging problem, as it requires reasoning over both discrete contact modes and continuous object motion. We introduce Imp…