9 papers
Explore, Execute, Evolve: A Skill Acquisition and Reuse Loop for Embodied Agents
Sicheng Xie, Yitong Chen, Haidong Cao +3
Vision-language-action and world-action models have demonstrated impressive capabilities in robotics, yet generalization to unseen tasks remains challenging. More recently, general…
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…
GuidedVLA: Specifying Task-Relevant Factors via Plug-and-Play Action Attention Specialization
Xiaosong Jia, Bowen Yang, Zuhao Ge +17
Vision-Language-Action (VLA) models aim for general robot learning by aligning action as a modality within powerful Vision-Language Models (VLMs). Existing VLAs rely on end-to-end…
FlashMotion: Few-Step Controllable Video Generation with Trajectory Guidance
Quanhao Li, Zhen Xing, Rui Wang +4
Recent advances in trajectory-controllable video generation have achieved remarkable progress. Previous methods mainly use adapter-based architectures for precise motion control al…
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…
HiMoE-VLA: Hierarchical Mixture-of-Experts for Generalist Vision-Language-Action Policies
Zhiying Du, Bei Liu, Yaobo Liang +7
Generalist vision--language--action (VLA) policies are typically trained on heterogeneous mixtures of robot demonstrations spanning diverse embodiments, action spaces, and observat…
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…
Multi-Prompt Progressive Alignment for Multi-Source Unsupervised Domain Adaptation
Haoran Chen, Zexiao Wang, Haidong Cao +2
Large Vision-Language Models like CLIP have become a powerful foundation for Unsupervised Domain Adaptation due to their strong zero-shot generalization. State-of-the-art methods t…
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…