1 citations · 1 across the 6 of their papers we have counts for
13 papers
DepState-JEPA: Learning Depth-Grounded State Representation for Visual Perception and Manipulation
Xiujian Liang, Liuyuan Na, Zehao Du +6
Predictive representations for robot manipulation should connect scene geometry with the state changes induced by actions. Our intuition is that understanding what an action will c…
Can World and Action Modeling Benefit Each Other under Unified Causal Training?
Yue Su, Bokai Lin, Xinkai Wang +9
World and action modeling are closely related, yet their reciprocal benefits under unified causal training remain unclear. We introduce OneWM, a simple framework for unified causal…
ObjDream: Guiding Vision-Language-Action Models with Object-Level Understanding and Future State Prediction
Huailiang Ma, Yuxuan Li, Ziyi Zhao +7
Vision-language-action (VLA) models have emerged as a powerful paradigm for robotic manipulation, enabling robots to generate actions from visual observations and language instruct…
PredActor: Predictive Action Diffusion for Steerable Onboard Humanoid Control
Lei Ye, Haibo Gao, Yitang Li +10
Diffusion models provide a flexible framework for motion generation, but turning this flexibility into closed-loop humanoid control remains challenging. Hierarchical generator-trac…
Rethinking Visual Embodiment Dependence in Visuomotor Policies
Hongjie Fang, Yuxuan Lu, Chenxi Wang +8
Visuomotor policies observe both the task scene and the acting embodiment, allowing embodiment-specific visual cues to influence action prediction. We study this phenomenon as visu…
PO-PDDL: Learning Symbolic POMDPs from Visual Demonstrations for Robot Planning Under Uncertainty
Wenjing Tang, Xuanjin Jin, Yuan Liu +3
Real-world robot task planning must operate under both stochastic action execution and partial observability, yet constructing Partially Observable Markov Decision Process (POMDP)…
Mimic Intent, Not Just Trajectories
Renming Huang, Chendong Zeng, Wenjing Tang +3
While imitation learning (IL) has achieved impressive success in dexterous manipulation through generative modeling and pretraining, state-of-the-art approaches like Vision-Languag…
UniPlan: Vision-Language Task Planning for Mobile Manipulation with Unified PDDL Formulation
Haoming Ye, Yunxiao Xiao, Cewu Lu +1
Integration of VLM reasoning with symbolic planning has proven to be a promising approach to real-world robot task planning. Existing work like UniDomain effectively learns symboli…
TGPO: Trace-Guided Policy Optimization for Robot Task Planning via Verifiable Subgoal Generation
Zhihong Liu, Yang Li, Rengming Huang +4
Robot task planning in real-world environments requires mapping abstract natural language instructions to executable action sequences under long horizons and complex constraints. W…
UniDomain: Pretraining a Unified PDDL Domain from Real-World Demonstrations for Generalizable Robot Task Planning
Haoming Ye, Yunxiao Xiao, Cewu Lu +1
Robotic task planning in real-world environments requires reasoning over implicit constraints from language and vision. While LLMs and VLMs offer strong priors, they struggle with…
Tru-POMDP: Task Planning Under Uncertainty via Tree of Hypotheses and Open-Ended POMDPs
Wenjing Tang, Xinyu He, Yongxi Huang +3
Task planning under uncertainty is essential for home-service robots operating in the real world. Tasks involve ambiguous human instructions, hidden or unknown object locations, an…
ForceVLA: Enhancing VLA Models with a Force-aware MoE for Contact-rich Manipulation
Jiawen Yu, Hairuo Liu, Qiaojun Yu +9
Vision-Language-Action (VLA) models have advanced general-purpose robotic manipulation by leveraging pretrained visual and linguistic representations. However, they struggle with c…
Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly Detection
Shoubin Yu, Zhongyin Zhao, Haoshu Fang +6
Anomaly detection in surveillance videos is challenging and important for ensuring public security. Different from pixel-based anomaly detection methods, pose-based methods utilize…