3 citations · 10 across the 12 of their papers we have counts for
13 papers
SwitchVLA: Execution-Aware Task Switching for Vision-Language-Action Models
Meng Li, Zhen Zhao, Zhengping Che +7
Robots deployed in dynamic environments must be able to not only follow diverse language instructions but flexibly adapt when user intent changes mid-execution. While recent Vision…
SEEA-R1: Tree-Structured Reinforcement Fine-Tuning for Self-Evolving Embodied Agents
Wanxin Tian, Shijie Zhang, Kevin Zhang +12
Self-evolution, the ability of agents to autonomously improve their reasoning and behavior, is essential for the embodied domain with long-horizon, real-world tasks. Despite curren…
FreqPolicy: Efficient Flow-based Visuomotor Policy via Frequency Consistency
Yifei Su, Ning Liu, Dong Chen +6
Generative modeling-based visuomotor policies have been widely adopted in robotic manipulation, attributed to their ability to model multimodal action distributions. However, the h…
HACTS: a Human-As-Copilot Teleoperation System for Robot Learning
Zhiyuan Xu, Yinuo Zhao, Kun Wu +5
Teleoperation is essential for autonomous robot learning, especially in manipulation tasks that require human demonstrations or corrections. However, most existing systems only off…
ChatVLA: Unified Multimodal Understanding and Robot Control with Vision-Language-Action Model
Zhongyi Zhou, Yichen Zhu, Minjie Zhu +8
Humans possess a unified cognitive ability to perceive, comprehend, and interact with the physical world. Why can't large language models replicate this holistic understanding? Thr…
EDT: An Efficient Diffusion Transformer Framework Inspired by Human-like Sketching
Xinwang Chen, Ning Liu, Yichen Zhu +2
Transformer-based Diffusion Probabilistic Models (DPMs) have shown more potential than CNN-based DPMs, yet their extensive computational requirements hinder widespread practical ap…
Discrete Policy: Learning Disentangled Action Space for Multi-Task Robotic Manipulation
Kun Wu, Yichen Zhu, Jinming Li +4
Learning visuomotor policy for multi-task robotic manipulation has been a long-standing challenge for the robotics community. The difficulty lies in the diversity of action space:…
Scaling Diffusion Policy in Transformer to 1 Billion Parameters for Robotic Manipulation
Minjie Zhu, Yichen Zhu, Jinming Li +8
Diffusion Policy is a powerful technique tool for learning end-to-end visuomotor robot control. It is expected that Diffusion Policy possesses scalability, a key attribute for deep…
TinyVLA: Towards Fast, Data-Efficient Vision-Language-Action Models for Robotic Manipulation
Junjie Wen, Yichen Zhu, Jinming Li +9
Vision-Language-Action (VLA) models have shown remarkable potential in visuomotor control and instruction comprehension through end-to-end learning processes. However, current VLA…
Mipha: A Comprehensive Overhaul of Multimodal Assistant with Small Language Models
Minjie Zhu, Yichen Zhu, Xin Liu +7
Multimodal Large Language Models (MLLMs) have showcased impressive skills in tasks related to visual understanding and reasoning. Yet, their widespread application faces obstacles…
LLaVA-Phi: Efficient Multi-Modal Assistant with Small Language Model
Yichen Zhu, Minjie Zhu, Ning Liu +3
In this paper, we introduce LLaVA- (LLaVA-Phi), an efficient multi-modal assistant that harnesses the power of the recently advanced small language model, Phi-2, to facilitate m…
EPSD: Early Pruning with Self-Distillation for Efficient Model Compression
Dong Chen, Ning Liu, Yichen Zhu +6
Neural network compression techniques, such as knowledge distillation (KD) and network pruning, have received increasing attention. Recent work `Prune, then Distill' reveals that a…
Learning from Imperfect Demonstrations with Self-Supervision for Robotic Manipulation
Kun Wu, Ning Liu, Zhen Zhao +5
Improving data utilization, especially for imperfect data from task failures, is crucial for robotic manipulation due to the challenging, time-consuming, and expensive data collect…