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Learning for a robot: Deep reinforcement learning, imitation learning, transfer learning
J Hua, L Zeng, G Li, Z Ju - Sensors, 2021 - mdpi.com
Dexterous manipulation of the robot is an important part of realizing intelligence, but
manipulators can only perform simple tasks such as sorting and packing in a structured …
manipulators can only perform simple tasks such as sorting and packing in a structured …
A review of robot learning for manipulation: Challenges, representations, and algorithms
O Kroemer, S Niekum, G Konidaris - Journal of machine learning research, 2021 - jmlr.org
A key challenge in intelligent robotics is creating robots that are capable of directly
interacting with the world around them to achieve their goals. The last decade has seen …
interacting with the world around them to achieve their goals. The last decade has seen …
Unidexgrasp++: Improving dexterous gras** policy learning via geometry-aware curriculum and iterative generalist-specialist learning
We propose a novel, object-agnostic method for learning a universal policy for dexterous
object gras** from realistic point cloud observations and proprioceptive information under …
object gras** from realistic point cloud observations and proprioceptive information under …
Q-learning algorithms: A comprehensive classification and applications
B Jang, M Kim, G Harerimana, JW Kim - IEEE access, 2019 - ieeexplore.ieee.org
Q-learning is arguably one of the most applied representative reinforcement learning
approaches and one of the off-policy strategies. Since the emergence of Q-learning, many …
approaches and one of the off-policy strategies. Since the emergence of Q-learning, many …
Deepgauge: Multi-granularity testing criteria for deep learning systems
L Ma, F Juefei-Xu, F Zhang, J Sun, M Xue, B Li… - Proceedings of the 33rd …, 2018 - dl.acm.org
Deep learning (DL) defines a new data-driven programming paradigm that constructs the
internal system logic of a crafted neuron network through a set of training data. We have …
internal system logic of a crafted neuron network through a set of training data. We have …
Robust physical-world attacks on deep learning visual classification
K Eykholt, I Evtimov, E Fernandes… - Proceedings of the …, 2018 - openaccess.thecvf.com
Recent studies show that the state-of-the-art deep neural networks (DNNs) are vulnerable to
adversarial examples, resulting from small-magnitude perturbations added to the input …
adversarial examples, resulting from small-magnitude perturbations added to the input …
Runtime neural pruning
In this paper, we propose a Runtime Neural Pruning (RNP) framework which prunes the
deep neural network dynamically at the runtime. Unlike existing neural pruning methods …
deep neural network dynamically at the runtime. Unlike existing neural pruning methods …
The limits and potentials of deep learning for robotics
N Sünderhauf, O Brock, W Scheirer… - … journal of robotics …, 2018 - journals.sagepub.com
The application of deep learning in robotics leads to very specific problems and research
questions that are typically not addressed by the computer vision and machine learning …
questions that are typically not addressed by the computer vision and machine learning …
Deepmutation: Mutation testing of deep learning systems
Deep learning (DL) defines a new data-driven programming paradigm where the internal
system logic is largely shaped by the training data. The standard way of evaluating DL …
system logic is largely shaped by the training data. The standard way of evaluating DL …
[PDF][PDF] Robust physical-world attacks on machine learning models
I Evtimov, K Eykholt, E Fernandes… - arxiv preprint arxiv …, 2017 - s3.observador.pt
Deep neural network-based classifiers are known to be vulnerable to adversarial examples
that can fool them into misclassifying their input through the addition of small-magnitude …
that can fool them into misclassifying their input through the addition of small-magnitude …