1 citations · 1 across the 9 of their papers we have counts for
10 papers
SiMDex: Mining Similar Egocentric Videos for Cross-Embodiment Dexterous Manipulation
Nie Lin, Takehiko Ohkawa, Sijin Chen +10
Recent years have witnessed an explosive trend of scaling ego-centric human videos for robot manipulation, yet it remains unclear which data actually benefits dexterous manipulatio…
FLAC: Maximum Entropy RL via Kinetic Energy Regularized Bridge Matching
Lei Lv, Yunfei Li, Yu Luo +2
Iterative generative policies, such as diffusion models and flow matching, offer superior expressivity for continuous control but complicate Maximum Entropy Reinforcement Learning…
GR-Dexter Technical Report
Ruoshi Wen, Guangzeng Chen, Zhongren Cui +23
Vision-language-action (VLA) models have enabled language-conditioned, long-horizon robot manipulation, but most existing systems are limited to grippers. Scaling VLA policies to b…
Flow-Based Policy for Online Reinforcement Learning
Lei Lv, Yunfei Li, Yu Luo +4
We present \textbf{FlowRL}, a novel framework for online reinforcement learning that integrates flow-based policy representation with Wasserstein-2-regularized optimization. We arg…
What Limits Virtual Agent Application? OmniBench: A Scalable Multi-Dimensional Benchmark for Essential Virtual Agent Capabilities
Wendong Bu, Yang Wu, Qifan Yu +10
As multimodal large language models (MLLMs) advance, MLLM-based virtual agents have demonstrated remarkable performance. However, existing benchmarks face significant limitations,…
FocusDiff: Advancing Fine-Grained Text-Image Alignment for Autoregressive Visual Generation through RL
Kaihang Pan, Wendong Bu, Yuruo Wu +7
Recent studies extend the autoregression paradigm to text-to-image generation, achieving performance comparable to diffusion models. However, our new PairComp benchmark -- featurin…
Janus-Pro-R1: Advancing Collaborative Visual Comprehension and Generation via Reinforcement Learning
Kaihang Pan, Yang Wu, Wendong Bu +9
Recent endeavors in Multimodal Large Language Models (MLLMs) aim to unify visual comprehension and generation. However, these two capabilities remain largely independent, as if the…
Boosting Virtual Agent Learning and Reasoning: A Step-Wise, Multi-Dimensional, and Generalist Reward Model with Benchmark
Bingchen Miao, Yang Wu, Minghe Gao +7
The development of Generalist Virtual Agents (GVAs) has shown significant promise in autonomous task execution. However, current training paradigms face critical limitations, inclu…
Mastering Collaborative Multi-modal Data Selection: A Focus on Informativeness, Uniqueness, and Representativeness
Qifan Yu, Zhebei Shen, Zhongqi Yue +7
Instruction tuning fine-tunes pre-trained Multi-modal Large Language Models (MLLMs) to handle real-world tasks. However, the rapid expansion of visual instruction datasets introduc…
Generalist Virtual Agents: A Survey on Autonomous Agents Across Digital Platforms
Minghe Gao, Wendong Bu, Bingchen Miao +7
In this paper, we introduce the Generalist Virtual Agent (GVA), an autonomous entity engineered to function across diverse digital platforms and environments, assisting users by ex…