32 research outputs found
Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks
Autonomous robots need to interact with unknown, unstructured and changing
environments, constantly facing novel challenges. Therefore, continuous online
adaptation for lifelong-learning and the need of sample-efficient mechanisms to
adapt to changes in the environment, the constraints, the tasks, or the robot
itself are crucial. In this work, we propose a novel framework for
probabilistic online motion planning with online adaptation based on a
bio-inspired stochastic recurrent neural network. By using learning signals
which mimic the intrinsic motivation signalcognitive dissonance in addition
with a mental replay strategy to intensify experiences, the stochastic
recurrent network can learn from few physical interactions and adapts to novel
environments in seconds. We evaluate our online planning and adaptation
framework on an anthropomorphic KUKA LWR arm. The rapid online adaptation is
shown by learning unknown workspace constraints sample-efficiently from few
physical interactions while following given way points.Comment: accepted in Neural Network
Integrating Human Expertise in Continuous Spaces: A Novel Interactive Bayesian Optimization Framework with Preference Expected Improvement
Interactive Machine Learning (IML) seeks to integrate human expertise into
machine learning processes. However, most existing algorithms cannot be applied
to Realworld Scenarios because their state spaces and/or action spaces are
limited to discrete values. Furthermore, the interaction of all existing
methods is restricted to deciding between multiple proposals. We therefore
propose a novel framework based on Bayesian Optimization (BO). Interactive
Bayesian Optimization (IBO) enables collaboration between machine learning
algorithms and humans. This framework captures user preferences and provides an
interface for users to shape the strategy by hand. Additionally, we've
incorporated a new acquisition function, Preference Expected Improvement (PEI),
to refine the system's efficiency using a probabilistic model of the user
preferences. Our approach is geared towards ensuring that machines can benefit
from human expertise, aiming for a more aligned and effective learning process.
In the course of this work, we applied our method to simulations and in a real
world task using a Franka Panda robot to show human-robot collaboration
CR-VAE: Contrastive Regularization on Variational Autoencoders for Preventing Posterior Collapse
The Variational Autoencoder (VAE) is known to suffer from the phenomenon of
\textit{posterior collapse}, where the latent representations generated by the
model become independent of the inputs. This leads to degenerated
representations of the input, which is attributed to the limitations of the
VAE's objective function. In this work, we propose a novel solution to this
issue, the Contrastive Regularization for Variational Autoencoders (CR-VAE).
The core of our approach is to augment the original VAE with a contrastive
objective that maximizes the mutual information between the representations of
similar visual inputs. This strategy ensures that the information flow between
the input and its latent representation is maximized, effectively avoiding
posterior collapse. We evaluate our method on a series of visual datasets and
demonstrate, that CR-VAE outperforms state-of-the-art approaches in preventing
posterior collapse
Understanding why SLAM algorithms fail in modern indoor environments
Simultaneous localization and mapping (SLAM) algorithms are essential for the
autonomous navigation of mobile robots. With the increasing demand for
autonomous systems, it is crucial to evaluate and compare the performance of
these algorithms in real-world environments. In this paper, we provide an
evaluation strategy and real-world datasets to test and evaluate SLAM
algorithms in complex and challenging indoor environments. Further, we analysed
state-of-the-art (SOTA) SLAM algorithms based on various metrics such as
absolute trajectory error, scale drift, and map accuracy and consistency. Our
results demonstrate that SOTA SLAM algorithms often fail in challenging
environments, with dynamic objects, transparent and reflecting surfaces. We
also found that successful loop closures had a significant impact on the
algorithm's performance. These findings highlight the need for further research
to improve the robustness of the algorithms in real-world scenarios
Low-cost Sensor Glove with Force Feedback for Learning from Demonstrations using Probabilistic Trajectory Representations
Sensor gloves are popular input devices for a large variety of applications
including health monitoring, control of music instruments, learning sign
language, dexterous computer interfaces, and tele-operating robot hands. Many
commercial products as well as low-cost open source projects have been
developed. We discuss here how low-cost (approx. 250 EUROs) sensor gloves with
force feedback can be build, provide an open source software interface for
Matlab and present first results in learning object manipulation skills through
imitation learning on the humanoid robot iCub.Comment: 3 pages, 3 figures. Workshop paper of the International Conference on
Robotics and Automation (ICRA 2015
O2S: Open-source open shuttle
Currently, commercially available intelligent transport robots that are
capable of carrying up to 90kg of load can cost \1500. Furthermore,
O2S offers a simple yet robust framework for contextualizing simultaneous
localization and mapping (SLAM) algorithms, an essential prerequisite for
autonomous robot navigation. The robustness and performance of the O2S were
validated through real-world and simulation experiments. All the design,
construction and software files are freely available online under the GNU GPL
v3 license at https://doi.org/10.17605/OSF.IO/K83X7. A descriptive video of O2S
can be found at https://osf.io/v8tq2