2 citations · 2 across the 8 of their papers we have counts for
7 papers · 1 filter
GLST: Global-Local Spatio-Temporal Memory Decomposition Framework for Online Chained-Goal Navigation
Yousef Liang, Ryan Ardalan, Yarden Goraly +6
Autonomous navigation in real-world environments often requires long-horizon operation under partial observability and evolving goals. Existing mapless navigation approaches predom…
TouchScale: 500 Hours of Human Vision and Touch for Visual-Tactile Learning
Dayou Li, Hao Wang, Qianqian Yang +23
Large-scale egocentric human interaction data is becoming an important source of physical supervision for embodied learning, yet video alone leaves the contact and pressure that ch…
Co-Evolving Robot Orchestrators and Policies through Deployment
Xilun Zhang, Maggie Wang, Erik Bauer +4
Vision-language-action (VLA) policies trained on large datasets are capable within their training domains, yet they still fail to generalize to the variety of situations a robot me…
DeepJEPA: Scaling World Models from Within
Zijian Jin, Yunbei Zhang, Yuanzhe Liu +5
World-model planners typically scale outward by rolling farther, sampling more trajectories, or optimizing longer, while assigning the same computation to every imagined transition…
LIBERO-MAX: Do Robot Policies Adapt When the World Changes?
Yunbei Zhang, Zijian Jin, Yuanzhe Liu +14
Robots must often continue a task after a target moves, the viewpoint shifts, or an obstacle appears, even though their earlier observations and committed actions reflect the previ…
Structured World-State Reasoning for Agentic Robotic Search
Finley R. Holt, Luis A. Pabon, John Irvin Alora +2
Long-horizon robotic search must resolve natural language against heterogeneous, incomplete, and often ambiguous evidence: textual information, prior maps, and observations arrivin…
Pose-Agnostic Robotic Functional Grasping via Observation-Action Canonicalization
Le Qiu, Cole Harrison, Jiankai Sun +5
Functional robotic grasping requires a policy that generalizes across diverse object geometries and poses while maintaining task-specific contact precision. We study this challenge…