5,268 research outputs found
Intuitive Hand Teleoperation by Novice Operators Using a Continuous Teleoperation Subspace
Human-in-the-loop manipulation is useful in when autonomous grasping is not
able to deal sufficiently well with corner cases or cannot operate fast enough.
Using the teleoperator's hand as an input device can provide an intuitive
control method but requires mapping between pose spaces which may not be
similar. We propose a low-dimensional and continuous teleoperation subspace
which can be used as an intermediary for mapping between different hand pose
spaces. We present an algorithm to project between pose space and teleoperation
subspace. We use a non-anthropomorphic robot to experimentally prove that it is
possible for teleoperation subspaces to effectively and intuitively enable
teleoperation. In experiments, novice users completed pick and place tasks
significantly faster using teleoperation subspace mapping than they did using
state of the art teleoperation methods.Comment: ICRA 2018, 7 pages, 7 figures, 2 table
Intelligent manipulation technique for multi-branch robotic systems
New analytical development in kinematics planning is reported. The INtelligent KInematics Planner (INKIP) consists of the kinematics spline theory and the adaptive logic annealing process. Also, a novel framework of robot learning mechanism is introduced. The FUzzy LOgic Self Organized Neural Networks (FULOSONN) integrates fuzzy logic in commands, control, searching, and reasoning, the embedded expert system for nominal robotics knowledge implementation, and the self organized neural networks for the dynamic knowledge evolutionary process. Progress on the mechanical construction of SRA Advanced Robotic System (SRAARS) and the real time robot vision system is also reported. A decision was made to incorporate the Local Area Network (LAN) technology in the overall communication system
Neural Field Movement Primitives for Joint Modelling of Scenes and Motions
This paper presents a novel Learning from Demonstration (LfD) method that
uses neural fields to learn new skills efficiently and accurately. It achieves
this by utilizing a shared embedding to learn both scene and motion
representations in a generative way. Our method smoothly maps each expert
demonstration to a scene-motion embedding and learns to model them without
requiring hand-crafted task parameters or large datasets. It achieves data
efficiency by enforcing scene and motion generation to be smooth with respect
to changes in the embedding space. At inference time, our method can retrieve
scene-motion embeddings using test time optimization, and generate precise
motion trajectories for novel scenes. The proposed method is versatile and can
employ images, 3D shapes, and any other scene representations that can be
modeled using neural fields. Additionally, it can generate both end-effector
positions and joint angle-based trajectories. Our method is evaluated on tasks
that require accurate motion trajectory generation, where the underlying task
parametrization is based on object positions and geometric scene changes.
Experimental results demonstrate that the proposed method outperforms the
baseline approaches and generalizes to novel scenes. Furthermore, in real-world
experiments, we show that our method can successfully model multi-valued
trajectories, it is robust to the distractor objects introduced at inference
time, and it can generate 6D motions.Comment: Accepted to IROS 2023. 8 pages, 7 figures, 2 tables. Project Page:
https://fzaero.github.io/NFMP
CASSL: Curriculum Accelerated Self-Supervised Learning
Recent self-supervised learning approaches focus on using a few thousand data
points to learn policies for high-level, low-dimensional action spaces.
However, scaling this framework for high-dimensional control require either
scaling up the data collection efforts or using a clever sampling strategy for
training. We present a novel approach - Curriculum Accelerated Self-Supervised
Learning (CASSL) - to train policies that map visual information to high-level,
higher- dimensional action spaces. CASSL orders the sampling of training data
based on control dimensions: the learning and sampling are focused on few
control parameters before other parameters. The right curriculum for learning
is suggested by variance-based global sensitivity analysis of the control
space. We apply our CASSL framework to learning how to grasp using an adaptive,
underactuated multi-fingered gripper, a challenging system to control. Our
experimental results indicate that CASSL provides significant improvement and
generalization compared to baseline methods such as staged curriculum learning
(8% increase) and complete end-to-end learning with random exploration (14%
improvement) tested on a set of novel objects
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