1 citations · 2 across the 15 of their papers we have counts for
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Conditional Trajectory Peaks: Single-Pass Multimodal Policies over Action Chunks
Di Wu, Rongtian Shen, Ping Liu +4
Multimodal imitation learning requires diverse executable futures under the same observation and consistent behavior across replanning cycles. We present Conditional Trajectory Pea…
Execution-Aligned Progressive Noise for Consistent Asynchronous Replanning in Generative Robot Policies
Di Wu, Ping Liu, Xuhua Chen +3
Continuous asynchronous replanning is essential for real-time generative robot policies, but independent stochastic initialization can cause mode switching and inconsistent continu…
Magic-W0: A Structured World-Action Foundation Model for Physical Intelligence
Xuhua Chen, Zhenhan Yin, Yuan Zhang +15
World-action models (WAMs) augment robot policies with action-conditioned environment dynamics, yet existing approaches largely rely on future observation reconstruction or generic…
What Matters for Latent Actions in Robot Learning
Xizhou Bu, Qingda Hu, Lei Zhou +13
Latent Action Models (LAMs) have emerged as a promising paradigm for enabling robot learning to leverage large-scale unlabeled videos through latent actions that serve as compact s…
CheckVLA: Execution-Time Verification with Action-Conditioned World Model for Long-Horizon Mobile Manipulation
Yushan Liu, Peibo Sun, Xintao Chao +8
Vision-language-action (VLA) policies commonly execute long-horizon mobile manipulation through open-loop action chunks, issuing multiple actions without receiving new high-level v…
Anticipate Before Acting: Future-State-Conditioned Vision-Language Navigation
Lingfeng Zhang, Zhanguang Zhang, Liheng Ma +2
End-to-end vision-language navigation (VLN) with causal vision-language models maps instructions and egocentric observations directly to actions, but standard behavior cloning supe…
OneVLA: A Unified Framework for Embodied Tasks
Lingfeng Zhang, Xiaoshuai Hao, Yingbo Tang +10
Navigation and manipulation are fundamental capabilities of embodied intelligence, enabling robots to interpret natural language commands and interact physically with their surroun…
OA-WAM: Object-Addressable World Action Model for Robust Robot Manipulation
Yushan Liu, Peibo Sun, Shoujie Li +7
World Action Models (WAMs) enhance Vision-Language-Action policies by jointly predicting scene evolution and robot actions, but existing methods usually represent the predicted wor…
Thinking in Text and Images: Interleaved Vision--Language Reasoning Traces for Long-Horizon Robot Manipulation
Jinkun Liu, Haohan Chi, Lingfeng Zhang +6
Long-horizon robotic manipulation requires plans that are both logically coherent and geometrically grounded. Existing Vision-Language-Action policies usually hide planning in late…
Walk With Me: Long-Horizon Social Navigation for Human-Centric Outdoor Assistance
Lingfeng Zhang, Xiaoshuai Hao, Xizhou Bu +11
Assisting humans in open-world outdoor environments requires robots to translate high-level natural-language intentions into safe, long-horizon, and socially compliant navigation b…
Do World Action Models Generalize Better than VLAs? A Robustness Study
Zhanguang Zhang, Zhiyuan Li, Behnam Rahmati +11
Robot action planning in the real world is challenging as it requires not only understanding the current state of the environment but also predicting how it will evolve in response…