Search Results for author:
Found 9 papers, 6 papers with code
The ACRV Picking Benchmark (APB): A Robotic Shelf Picking Benchmark to Foster Reproducible Research
We present a new physical benchmark challenge for robotic picking: the ACRV Picking Benchmark (APB).
One-Shot Reinforcement Learning for Robot Navigation with Interactive Replay
Recently, model-free reinforcement learning algorithms have been shown to solve challenging problems by learning from extensive interaction with the environment.
LoST? Appearance-Invariant Place Recognition for Opposite Viewpoints using Visual Semantics
Human visual scene understanding is so remarkable that we are able to recognize a revisited place when entering it from the opposite direction it was first visited, even in the presence of extreme variations in appearance.
Structure Aware SLAM using Quadrics and Planes
Simultaneous Localization And Mapping (SLAM) is a fundamental problem in mobile robotics.
Look No Deeper: Recognizing Places from Opposing Viewpoints under Varying Scene Appearance using Single-View Depth Estimation
Visual place recognition (VPR) - the act of recognizing a familiar visual place - becomes difficult when there is extreme environmental appearance change or viewpoint change.
Robotics
Critic Guided Segmentation of Rewarding Objects in First-Person Views
This work discusses a learning approach to mask rewarding objects in images using sparse reward signals from an imitation learning dataset.
The Need for Inherently Privacy-Preserving Vision in Trustworthy Autonomous Systems
Vision is a popular and effective sensor for robotics from which we can derive rich information about the environment: the geometry and semantics of the scene, as well as the age, gender, identity, activity and even emotional state of humans within that scene.
SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning
To ensure the scalability of our approach, we: (1) exploit the hierarchical nature of 3DSGs to allow LLMs to conduct a 'semantic search' for task-relevant subgraphs from a smaller, collapsed representation of the full graph; (2) reduce the planning horizon for the LLM by integrating a classical path planner and (3) introduce an 'iterative replanning' pipeline that refines the initial plan using feedback from a scene graph simulator, correcting infeasible actions and avoiding planning failures.
Human-in-the-Loop Segmentation of Multi-species Coral Imagery
For extremely sparsely labeled images, we propose a labeling regime based on human-in-the-loop principles, resulting in significant improvement in annotation efficiency: If only 5 point labels per image are available, our proposed human-in-the-loop approach improves on the state-of-the-art by 17. 3% for pixel accuracy and 22. 6% for mIoU; and by 10. 6% and 19. 1% when 10 point labels per image are available.
