195 research outputs found
Evaluating Robustness of Visual Representations for Object Assembly Task Requiring Spatio-Geometrical Reasoning
This paper primarily focuses on evaluating and benchmarking the robustness of
visual representations in the context of object assembly tasks. Specifically,
it investigates the alignment and insertion of objects with geometrical
extrusions and intrusions, commonly referred to as a peg-in-hole task. The
accuracy required to detect and orient the peg and the hole geometry in SE(3)
space for successful assembly poses significant challenges. Addressing this, we
employ a general framework in visuomotor policy learning that utilizes visual
pretraining models as vision encoders. Our study investigates the robustness of
this framework when applied to a dual-arm manipulation setup, specifically to
the grasp variations. Our quantitative analysis shows that existing pretrained
models fail to capture the essential visual features necessary for this task.
However, a visual encoder trained from scratch consistently outperforms the
frozen pretrained models. Moreover, we discuss rotation representations and
associated loss functions that substantially improve policy learning. We
present a novel task scenario designed to evaluate the progress in visuomotor
policy learning, with a specific focus on improving the robustness of intricate
assembly tasks that require both geometrical and spatial reasoning. Videos,
additional experiments, dataset, and code are available at
https://bit.ly/geometric-peg-in-hole
相対座標における高速視覚フィードバックに基づくダイナミックコンペンセーション
学位の種別:課程博士University of Tokyo(東京大学
Vision-Based Automated Hole Assembly System with Quality Inspection
Automated manufacturing, driven by rising demands for mass-produced products, calls for efficient systems such as the peg-in-hole assembly. Traditional industrial robots perform these tasks but often fall short in speed during pick-and-place processes. This study presents an innovative mechatronic system for peg-in-hole assembly, integrating a novel peg insertion tool, assembly mechanism and control algorithm. This combination achieves peg insertion with a 200 µm tolerance without the need for pick-and-place, meeting the requirements for high precision and rapidity in modern manufacturing. Dual cameras and computer vision techniques, both traditional and machine learning (ML)-based, are employed to detect workpiece features essential for assembly. Traditional methods focus on image enhancement, edge detection and circular feature recognition, whereas ML verifies workpiece positions. This research also introduces a novel statistical quality inspection, offering an alternative to standard ML inspections. Through rigorous testing on varied workpiece surfaces, the robustness of the methods is affirmed. The assembly system demonstrates a 99.00% success rate, while the quality inspection method attains a 97.02% accuracy across diverse conditions, underscoring the potential of these techniques in automated assembly, defect detection and product quality assurance
Proceedings of the NASA Conference on Space Telerobotics, volume 2
These proceedings contain papers presented at the NASA Conference on Space Telerobotics held in Pasadena, January 31 to February 2, 1989. The theme of the Conference was man-machine collaboration in space. The Conference provided a forum for researchers and engineers to exchange ideas on the research and development required for application of telerobotics technology to the space systems planned for the 1990s and beyond. The Conference: (1) provided a view of current NASA telerobotic research and development; (2) stimulated technical exchange on man-machine systems, manipulator control, machine sensing, machine intelligence, concurrent computation, and system architectures; and (3) identified important unsolved problems of current interest which can be dealt with by future research
The Sixth Annual Workshop on Space Operations Applications and Research (SOAR 1992)
This document contains papers presented at the Space Operations, Applications, and Research Symposium (SOAR) hosted by the U.S. Air Force (USAF) on 4-6 Aug. 1992 and held at the JSC Gilruth Recreation Center. The symposium was cosponsored by the Air Force Material Command and by NASA/JSC. Key technical areas covered during the symposium were robotic and telepresence, automation and intelligent systems, human factors, life sciences, and space maintenance and servicing. The SOAR differed from most other conferences in that it was concerned with Government-sponsored research and development relevant to aerospace operations. The symposium's proceedings include papers covering various disciplines presented by experts from NASA, the USAF, universities, and industry
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