1,958 research outputs found
Safely Learning Visuo-Tactile Feedback Policies in Real For Industrial Insertion
Industrial insertion tasks are often performed repetitively with parts that
are subject to tight tolerances and prone to breakage. In this paper, we
present a safe method to learn a visuo-tactile insertion policy that is robust
against grasp pose variations while minimizing human inputs and collision
between the robot and the environment. We achieve this by dividing the
insertion task into two phases. In the first align phase, we learn a
tactile-based grasp pose estimation model to align the insertion part with the
receptacle. In the second insert phase, we learn a vision-based policy to guide
the part into the receptacle. Using force-torque sensing, we also develop a
safe self-supervised data collection pipeline that limits collision between the
part and the surrounding environment. Physical experiments on the USB insertion
task from the NIST Assembly Taskboard suggest that our approach can achieve
45/45 insertion successes on 45 different initial grasp poses, improving on two
baselines: (1) a behavior cloning agent trained on 50 human insertion
demonstrations (1/45) and (2) an online RL policy (TD3) trained in real (0/45)
Chain of refined perception in self-optimizing assembly of micro-optical systems
Today, the assembly of laser systems requires a large share of manual
operations due to its complexity regarding the optimal alignment of optics.
Although the feasibility of automated alignment of laser optics has been
shown in research labs, the development effort for the automation of
assembly does not meet economic requirements – especially for low-volume
laser production. This paper presents a model-based and sensor-integrated
assembly execution approach for flexible assembly cells consisting of a
macro-positioner covering a large workspace and a compact micromanipulator
with camera attached to the positioner. In order to make full use of
available models from computer-aided design (CAD) and optical simulation, sensor systems at different
levels of accuracy are used for matching perceived information with model
data. This approach is named "chain of refined perception", and it allows for
automated planning of complex assembly tasks along all major phases of
assembly such as collision-free path planning, part feeding, and active and
passive alignment. The focus of the paper is put on the in-process
image-based metrology and information extraction used for identifying and
calibrating local coordinate systems as well as the exploitation of that
information for a part feeding process for micro-optics. Results will be
presented regarding the processes of automated calibration of the robot
camera as well as the local coordinate systems of part feeding area and
robot base
Cyber-Physical Systems for Micro-/Nano-assembly Operations: a Survey
Abstract
Purpose of Review
Latest requirements of the global market force manufacturing systems to a change for a new production paradigm (Industry 4.0). Cyber-Physical Systems (CPS) appear as a solution to be deployed in different manufacturing fields, especially those with high added value and technological complexity, high product variants, and short time to market. In this sense, this paper aims at reviewing the introduction level of CPS technologies in micro/nano-manufacturing and how these technologies could cope with these challenging manufacturing requirements.
Recent Findings
The introduction of CPS is still in its infancy on many industrial applications, but it actually demonstrates its potential to support future manufacturing paradigm. However, only few research works in micro/nano-manufacturing considered CPS frameworks, since the concept barely appeared a decade ago.
Summary
Some contributions have revealed the potential of CPS technologies to improve manufacturing performance which may be scaled to the micro/nano-manufacturing. IoT-based frameworks with VR/AR technologies allow distributed and collaborative systems, or agent-based architectures with advance algorithm implementations that improve the flexibility and performance of micro-/nano-assembly operations. Future research of CPS in micro-/nano-assembly operations should be followed by more studies of its technical deployment showing its implications under other perspectives, i.e. sustainable, economic, and social point of views, to take full advance of all its features
Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2021
This Open Access proceedings presents a good overview of the current research landscape of assembly, handling and industrial robotics.
The objective of MHI Colloquium is the successful networking at both academic and management level. Thereby, the colloquium focuses an academic exchange at a high level in order to distribute the obtained research results, to determine synergy effects and trends, to connect the actors in person and in conclusion, to strengthen the research field as well as the MHI community. In addition, there is the possibility to become acquatined with the organizing institute. Primary audience is formed by members of the scientific society for assembly, handling and industrial robotics (WGMHI)
Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2021
This Open Access proceedings presents a good overview of the current research landscape of assembly, handling and industrial robotics. The objective of MHI Colloquium is the successful networking at both academic and management level. Thereby, the colloquium focuses an academic exchange at a high level in order to distribute the obtained research results, to determine synergy effects and trends, to connect the actors in person and in conclusion, to strengthen the research field as well as the MHI community. In addition, there is the possibility to become acquatined with the organizing institute. Primary audience is formed by members of the scientific society for assembly, handling and industrial robotics (WGMHI)
Structural Concept Learning via Graph Attention for Multi-Level Rearrangement Planning
Robotic manipulation tasks, such as object rearrangement, play a crucial role
in enabling robots to interact with complex and arbitrary environments.
Existing work focuses primarily on single-level rearrangement planning and,
even if multiple levels exist, dependency relations among substructures are
geometrically simpler, like tower stacking. We propose Structural Concept
Learning (SCL), a deep learning approach that leverages graph attention
networks to perform multi-level object rearrangement planning for scenes with
structural dependency hierarchies. It is trained on a self-generated simulation
data set with intuitive structures, works for unseen scenes with an arbitrary
number of objects and higher complexity of structures, infers independent
substructures to allow for task parallelization over multiple manipulators, and
generalizes to the real world. We compare our method with a range of classical
and model-based baselines to show that our method leverages its scene
understanding to achieve better performance, flexibility, and efficiency. The
dataset, supplementary details, videos, and code implementation are available
at: https://manavkulshrestha.github.io/sclComment: Accepted to Conference on Robot Learning (CoRL) 202
Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2021
This Open Access proceedings presents a good overview of the current research landscape of assembly, handling and industrial robotics. The objective of MHI Colloquium is the successful networking at both academic and management level. Thereby, the colloquium focuses an academic exchange at a high level in order to distribute the obtained research results, to determine synergy effects and trends, to connect the actors in person and in conclusion, to strengthen the research field as well as the MHI community. In addition, there is the possibility to become acquatined with the organizing institute. Primary audience is formed by members of the scientific society for assembly, handling and industrial robotics (WGMHI)
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