activity
20242026
collaborators

8 papers

cs.RO2026

QF3: Fast Flow RL with Filtered Q-Gradients

Chung Min Kim, Brent Yi, David McAllister +7

Flow policies have become a standard policy class for learning robot behaviors from demonstrations, but reinforcement learning is still critical for improving pre-trained flow poli…

cs.RO2026

Scalable Behavior Cloning with Open Data, Training, and Evaluation

Arthur Allshire, Himanshu Gaurav Singh, Ritvik Singh +15

We introduce ABC, a fully open-source stack for manipulation with behavior cloning. At its core is ABC-130K: the largest open-source teleoperation dataset to date, featuring 3,500…

cs.CV2026

LoV3D: Grounding Cognitive Prognosis Reasoning in Longitudinal 3D Brain MRI via Regional Volume Assessments

Zhaoyang Jiang, Zhizhong Fu, David McAllister +2

Longitudinal brain MRI is essential for characterizing the progression of neurological diseases such as Alzheimer's disease assessment. However, current deep-learning tools fragmen…

cs.CV2026

Finite Difference Flow Optimization for RL Post-Training of Text-to-Image Models

David McAllister, Miika Aittala, Tero Karras +4

Reinforcement learning (RL) has become a standard technique for post-training diffusion-based image synthesis models, as it enables learning from reward signals to explicitly impro…

cs.LG2025

Flow Matching Policy Gradients

David McAllister, Songwei Ge, Brent Yi +5

Flow-based generative models, including diffusion models, excel at modeling continuous distributions in high-dimensional spaces. In this work, we introduce Flow Policy Optimization…

cs.RO2025

Visual Imitation Enables Contextual Humanoid Control

Arthur Allshire, Hongsuk Choi, Junyi Zhang +7

How can we teach humanoids to climb staircases and sit on chairs using the surrounding environment context? Arguably, the simplest way is to just show them-casually capture a human…

cs.CV2025

Decentralized Diffusion Models

David McAllister, Matthew Tancik, Jiaming Song +1

Large-scale AI model training divides work across thousands of GPUs, then synchronizes gradients across them at each step. This incurs a significant network burden that only centra…

cs.CV2024

Rethinking Score Distillation as a Bridge Between Image Distributions

David McAllister, Songwei Ge, Jia-Bin Huang +4

Score distillation sampling (SDS) has proven to be an important tool, enabling the use of large-scale diffusion priors for tasks operating in data-poor domains. Unfortunately, SDS…