Search Results for author:
Found 6 papers, 3 papers with code
SAID-NeRF: Segmentation-AIDed NeRF for Depth Completion of Transparent Objects
Acquiring accurate depth information of transparent objects using off-the-shelf RGB-D cameras is a well-known challenge in Computer Vision and Robotics.
Cluttered Food Grasping with Adaptive Fingers and Synthetic-Data Trained Object Detection
To address this problem, we propose a method that trains purely on synthetic data and successfully transfers to the real world using sim2real methods by creating datasets of filled food trays using high-quality 3d models of real pieces of food for the training instance segmentation models.
Invisible Marker: Automatic Annotation of Segmentation Masks for Object Manipulation
Invisible marker is invisible under visible (regular) light conditions, but becomes visible under invisible light, such as ultraviolet (UV) light.
Deep Visuo-Tactile Learning: Estimation of Tactile Properties from Images
Estimation of tactile properties from vision, such as slipperiness or roughness, is important to effectively interact with the environment.
Robotics
Map-based Multi-Policy Reinforcement Learning: Enhancing Adaptability of Robots by Deep Reinforcement Learning
To solve this problem, we propose Map-based Multi-Policy Reinforcement Learning (MMPRL), which aims to search and store multiple policies that encode different behavioral features while maximizing the expected reward in advance of the environment change.
Interactively Picking Real-World Objects with Unconstrained Spoken Language Instructions
In this paper, we propose the first comprehensive system that can handle unconstrained spoken language and is able to effectively resolve ambiguity in spoken instructions.
