2 citations · 2 across the 8 of their papers we have counts for
8 papers
AM-Bench: A Modular Simulation Suite and Benchmark for Aerial Manipulation Policy Learning
Yutong Wang, Dongjae Lee, Xiaofeng Guo +9
Standardized benchmarks have played a central role in advancing robot manipulation learning, yet most focus on ground-supported manipulation systems, which limits their applicabili…
SCOPE: Field-of-View-Aware Path Planning in Unknown Space via Safety-Volume Certification
Junbin Yuan, Muqing Cao, Yunwoo Lee +2
Safe navigation with a body-mounted limited-field-of-view sensor requires the complete robot-inflated volume of an intended motion to be observed and verified free before execution…
Aerial Manipulation with Contact-Aware Onboard Perception and Hybrid Control
Yuanzhu Zhan, Yufei Jiang, Muqing Cao +1
Aerial manipulation (AM) promises to move Unmanned Aerial Vehicles (UAVs) beyond passive inspection to contact-rich tasks such as grasping, assembly, and in-situ maintenance. Most…
Hierarchical Planning for Long-Horizon Multi-Target Tracking Under Target Motion Uncertainty
Junbin Yuan, Brady Moon, Muqing Cao +1
Achieving persistent tracking of multiple dynamic targets over a large spatial area poses significant challenges for a single-robot system with constrained sensing capabilities. As…
UMI-on-Air: Embodiment-Aware Guidance for Embodiment-Agnostic Visuomotor Policies
Harsh Gupta, Xiaofeng Guo, Huy Ha +6
We introduce UMI-on-Air, a framework for embodiment-aware deployment of embodiment-agnostic manipulation policies. Our approach leverages diverse, unconstrained human demonstration…
Beyond Frame-wise Tracking: A Trajectory-based Paradigm for Efficient Point Cloud Tracking
BaiChen Fan, Yuanxi Cui, Jian Li +4
LiDAR-based 3D single object tracking (3D SOT) is a critical task in robotics and autonomous systems. Existing methods typically follow frame-wise motion estimation or a sequence-b…
PIPE Planner: Pathwise Information Gain with Map Predictions for Indoor Robot Exploration
Seungjae Baek, Brady Moon, Seungchan Kim +4
Autonomous exploration in unknown environments requires estimating the information gain of an action to guide planning decisions. While prior approaches often compute information g…
IA-TIGRIS: An Incremental and Adaptive Sampling-Based Planner for Online Informative Path Planning
Brady Moon, Nayana Suvarna, Andrew Jong +4
Planning paths that maximize information gain for robotic platforms has wide-ranging applications and significant potential impact. To effectively adapt to real-time data collectio…