8 papers
Where to Touch, How to Contact: A Hierarchical RL-MPC Framework for Geometry-Aware Sim-to-Real Manipulation
Zhixian Xie, Yu Xiang, Michael Posa +1
The paper introduces a hierarchical framework that uses a high‑level reinforcement learning policy to choose where a robot should touch an object and a low‑level contact‑implicit m…
Cross-Embodiment Robot Manipulation via a Unified Hand Action Space
Luis Felipe Casas, Robert Teal, Keval Shah +3
Robot manipulation policies are typically tied to specific robotic hand embodiments, limiting the transfer of learned behaviors across platforms with different kinematic structures…
VLA-REPLICA: A Low-Cost, Reproducible Benchmark for Real-World Evaluation of Vision-Language-Action Models
Alex S. Huang, Jiahui Zhang, Shiqing Tang +1
Vision-Language-Action (VLA) models have shown strong promise for general-purpose robotic manipulation, but their real-world evaluation remains limited by a lack of accessible, rep…
iTeach: In the Wild Interactive Teaching for Failure-Driven Adaptation of Robot Perception
Jishnu Jaykumar P, Cole Salvato, Vinaya Bomnale +2
Robotic perception models often fail when deployed in real-world environments due to out-of-distribution conditions such as clutter, occlusion, and novel object instances. Existing…
The Great March 100: 100 Detail-oriented Tasks for Evaluating Embodied AI Agents
Ziyu Wang, Chenyuan Liu, Yushun Xiang +16
Recently, with the rapid development of robot learning and imitation learning, numerous datasets and methods have emerged. However, these datasets and their task designs often lack…
V-HOP: Visuo-Haptic 6D Object Pose Tracking
Hongyu Li, Mingxi Jia, Tuluhan Akbulut +3
Humans naturally integrate vision and haptics for robust object perception during manipulation. The loss of either modality significantly degrades performance. Inspired by this mul…