A review of robot learning for manipulation: Challenges, representations, and algorithms
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 …
Review of deep reinforcement learning-based object grasping: Techniques, open challenges, and recommendations
MQ Mohammed, KL Chung, CS Chyi - IEEE Access, 2020 - ieeexplore.ieee.org
The motivation behind our work is to review and analyze the most relevant studies on deep
reinforcement learning-based object manipulation. Various studies are examined through a …
reinforcement learning-based object manipulation. Various studies are examined through a …
Learning synergies between pushing and grasping with self-supervised deep reinforcement learning
Skilled robotic manipulation benefits from complex synergies between non-prehensile (eg
pushing) and prehensile (eg grasping) actions: pushing can help rearrange cluttered objects …
pushing) and prehensile (eg grasping) actions: pushing can help rearrange cluttered objects …
Actionable models: Unsupervised offline reinforcement learning of robotic skills
We consider the problem of learning useful robotic skills from previously collected offline
data without access to manually specified rewards or additional online exploration, a setting …
data without access to manually specified rewards or additional online exploration, a setting …
Contactnets: Learning discontinuous contact dynamics with smooth, implicit representations
Common methods for learning robot dynamics assume motion is continuous, causing
unrealistic model predictions for systems undergoing discontinuous impact and stiction …
unrealistic model predictions for systems undergoing discontinuous impact and stiction …
Augmenting physical simulators with stochastic neural networks: Case study of planar pushing and bouncing
An efficient, generalizable physical simulator with universal uncertainty estimates has wide
applications in robot state estimation, planning, and control. In this paper, we build such a …
applications in robot state estimation, planning, and control. In this paper, we build such a …
Learning rigid dynamics with face interaction graph networks
Simulating rigid collisions among arbitrary shapes is notoriously difficult due to complex
geometry and the strong non-linearity of the interactions. While graph neural network (GNN) …
geometry and the strong non-linearity of the interactions. While graph neural network (GNN) …
Reactive planar non-prehensile manipulation with hybrid model predictive control
FR Hogan, A Rodriguez - The International Journal of …, 2020 - journals.sagepub.com
This article presents an offline solution and online approximation to the hybrid control
problem of planar non-prehensile manipulation. Hybrid dynamics and underactuation are …
problem of planar non-prehensile manipulation. Hybrid dynamics and underactuation are …
Dipn: Deep interaction prediction network with application to clutter removal
We propose a Deep Interaction Prediction Network (DIPN) for learning to predict complex
interactions that ensue as a robot end-effector pushes multiple objects, whose physical …
interactions that ensue as a robot end-effector pushes multiple objects, whose physical …
Meta-learning priors for efficient online bayesian regression
Gaussian Process (GP) regression has seen widespread use in robotics due to its
generality, simplicity of use, and the utility of Bayesian predictions. The predominant …
generality, simplicity of use, and the utility of Bayesian predictions. The predominant …