10 papers
LIBERO-Agent: Evaluating General-Purpose Agents for Direct Embodied Manipulation
Zijie Diao, Yitong Chen, Sicheng Xie +7
General-purpose agents can plan, use tools, and revise their behavior from feedback, but it remains unclear whether these capabilities transfer from digital environments to embodie…
SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation
Haidong Cao, Wenjun Cao, Quanhao Li +5
Imitation learning enables robots to acquire manipulation skills from demonstrations by mapping observations to actions. Existing approaches predict either short-horizon continuous…
Qwen-VLA: Unifying Vision-Language-Action Modeling across Tasks, Environments, and Robot Embodiments
Qiuyue Wang, Mingsheng Li, Jian Guan +37
Embodied intelligence is often studied through specialized models for individual tasks such as manipulation or navigation, resulting in fragmented capabilities and limited generali…
FineVLA: Fine-Grained Instruction Alignment for Steerable Vision-Language-Action Policies
Xintong Hu, Xuhong Huang, Jinyu Zhang +11
Vision-Language-Action (VLA) models are increasingly expected to not only complete robot tasks, but also follow human instructions about how those tasks should be executed. However…
Preference Score Distillation: Leveraging 2D Rewards to Align Text-to-3D Generation with Human Preference
Jiaqi Leng, Shuyuan Tu, Haidong Cao +4
Human preference alignment presents a critical yet underexplored challenge for diffusion models in text-to-3D generation. Existing solutions typically require task-specific fine-tu…
Unify Robot Actions in Camera Frame
Sicheng Xie, Lingchen Meng, Zijie Diao +9
Cross-embodiment robot learning requires a unified action representation with consistent semantics across robot platforms. Existing representations suffer from platform-specific in…
Ask-to-Clarify: Resolving Instruction Ambiguity through Multi-turn Dialogue
Xingyao Lin, Xinghao Zhu, Tianyi Lu +6
Embodied agents are intelligent systems designed to perceive, reason, and act within the physical world. While the robotics community has long strived to build such versatile agent…
Human2Robot: Learning Robot Actions from Paired Human-Robot Videos
Sicheng Xie, Haidong Cao, Zejia Weng +6
Distilling knowledge from human demonstrations is a promising way for robots to learn and act. Existing methods, which often rely on coarsely-aligned video pairs, are typically con…
MotionFollower: Editing Video Motion via Lightweight Score-Guided Diffusion
Shuyuan Tu, Qi Dai, Zihao Zhang +6
Despite impressive advancements in diffusion-based video editing models in altering video attributes, there has been limited exploration into modifying motion information while pre…
Synthesize, Diagnose, and Optimize: Towards Fine-Grained Vision-Language Understanding
Wujian Peng, Sicheng Xie, Zuyao You +2
Vision language models (VLM) have demonstrated remarkable performance across various downstream tasks. However, understanding fine-grained visual-linguistic concepts, such as attri…