5 papers
From Local Matches to Global Masks: Template-Guided Instance Detection and Segmentation in Open-World Scenes
Qifan Zhang, Sai Haneesh Allu, Jikai Wang +2
Detecting and segmenting novel object instances in open-world environments is a fundamental problem in robotic perception. Given only a small set of template images, a robot must l…
HRT1: One-Shot Human-to-Robot Trajectory Transfer for Mobile Manipulation
Sai Haneesh Allu, Jishnu Jaykumar P, Ninad Khargonkar +3
We introduce a novel system for human-to-robot trajectory transfer that enables robots to manipulate objects by learning from human demonstration videos. The system consists of fou…
Build Once, Monitor Continuously: Persistent Semantic Mapping via Autonomous Exploration and Open-Vocabulary Object Updates
Sai Haneesh Allu, Itay Kadosh, Tyler Summers +1
Persistent semantic monitoring of indoor spaces such as warehouses, hospitals, and offices requires a robot to repeatedly monitor an environment and track how objects change over t…
Grasping Trajectory Optimization with Point Clouds
Yu Xiang, Sai Haneesh Allu, Rohith Peddi +2
We introduce a new trajectory optimization method for robotic grasping based on a point-cloud representation of robots and task spaces. In our method, robots are represented by 3D…
SCENEREPLICA: Benchmarking Real-World Robot Manipulation by Creating Replicable Scenes
Ninad Khargonkar, Sai Haneesh Allu, Yangxiao Lu +3
We present a new reproducible benchmark for evaluating robot manipulation in the real world, specifically focusing on pick-and-place. Our benchmark uses the YCB objects, a commonly…