2 research outputs found
Multimodal Material Classification for Robots using Spectroscopy and High Resolution Texture Imaging
Material recognition can help inform robots about how to properly interact
with and manipulate real-world objects. In this paper, we present a multimodal
sensing technique, leveraging near-infrared spectroscopy and close-range high
resolution texture imaging, that enables robots to estimate the materials of
household objects. We release a dataset of high resolution texture images and
spectral measurements collected from a mobile manipulator that interacted with
144 household objects. We then present a neural network architecture that
learns a compact multimodal representation of spectral measurements and texture
images. When generalizing material classification to new objects, we show that
this multimodal representation enables a robot to recognize materials with
greater performance as compared to prior state-of-the-art approaches. Finally,
we present how a robot can combine this high resolution local sensing with
images from the robot's head-mounted camera to achieve accurate material
classification over a scene of objects on a table.Comment: IEEE/RSJ International Conference on Intelligent Robots and Systems
(IROS 2020), 8 pages, 10 figures, 5 table
Feature Guided Search for Creative Problem Solving Through Tool Construction
Robots in the real world should be able to adapt to unforeseen circumstances.
Particularly in the context of tool use, robots may not have access to the
tools they need for completing a task. In this paper, we focus on the problem
of tool construction in the context of task planning. We seek to enable robots
to construct replacements for missing tools using available objects, in order
to complete the given task. We introduce the Feature Guided Search (FGS)
algorithm that enables the application of existing heuristic search approaches
in the context of task planning, to perform tool construction efficiently. FGS
accounts for physical attributes of objects (e.g., shape, material) during the
search for a valid task plan. Our results demonstrate that FGS significantly
reduces the search effort over standard heuristic search approaches by
approximately 93% for tool construction.Comment: NOTE: This paper has been published with Frontiers in Robotics and
AI. Please see the following link for the most updated version:
https://www.frontiersin.org/articles/10.3389/frobt.2020.592382/ful