Search Results for author:
Found 18 papers, 9 papers with code
Safe Reinforcement Learning on the Constraint Manifold: Theory and Applications
Videos of the real robot experiments are available on the project website (https://puzeliu. github. io/TRO-ATACOM).
Sharing Knowledge in Multi-Task Deep Reinforcement Learning
We study the benefit of sharing representations among tasks to enable the effective use of deep neural networks in Multi-Task Reinforcement Learning.
Towards Transferring Tactile-based Continuous Force Control Policies from Simulation to Robot
The advent of tactile sensors in robotics has sparked many ideas on how robots can leverage direct contact measurements of their environment interactions to improve manipulation tasks.
Time-Efficient Reinforcement Learning with Stochastic Stateful Policies
Stateful policies play an important role in reinforcement learning, such as handling partially observable environments, enhancing robustness, or imposing an inductive bias directly into the policy structure.
LocoMuJoCo: A Comprehensive Imitation Learning Benchmark for Locomotion
Imitation Learning (IL) holds great promise for enabling agile locomotion in embodied agents.
LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning
Recent methods for imitation learning directly learn a $Q$-function using an implicit reward formulation rather than an explicit reward function.
Fast Kinodynamic Planning on the Constraint Manifold with Deep Neural Networks
Motion planning is a mature area of research in robotics with many well-established methods based on optimization or sampling the state space, suitable for solving kinematic motion planning.
Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interaction
Our proposed approach achieves state-of-the-art performance in simulated high-dimensional and dynamic tasks while avoiding collisions with the environment.
Object Structural Points Representation for Graph-based Semantic Monocular Localization and Mapping
In this paper, we propose the use of an efficient representation, based on structural points, for the geometry of objects to be used as landmarks in a monocular semantic SLAM system based on the pose-graph formulation.
Regularized Deep Signed Distance Fields for Reactive Motion Generation
Autonomous robots should operate in real-world dynamic environments and collaborate with humans in tight spaces.
Dimensionality Reduction and Prioritized Exploration for Policy Search
Black-box policy optimization is a class of reinforcement learning algorithms that explores and updates the policies at the parameter level.
Learning Stable Vector Fields on Lie Groups
Learning robot motions from demonstration requires models able to specify vector fields for the full robot pose when the task is defined in operational space.
An Empirical Analysis of Measure-Valued Derivatives for Policy Gradients
This estimator is unbiased, has low variance, and can be used with differentiable and non-differentiable function approximators.
Structured Policy Representation: Imposing Stability in arbitrarily conditioned dynamic systems
We present a new family of deep neural network-based dynamic systems.
ImitationFlow: Learning Deep Stable Stochastic Dynamic Systems by Normalizing Flows
We introduce ImitationFlow, a novel Deep generative model that allows learning complex globally stable, stochastic, nonlinear dynamics.
Continuous Action Reinforcement Learning from a Mixture of Interpretable Experts
Reinforcement learning (RL) has demonstrated its ability to solve high dimensional tasks by leveraging non-linear function approximators.
MushroomRL: Simplifying Reinforcement Learning Research
MushroomRL is an open-source Python library developed to simplify the process of implementing and running Reinforcement Learning (RL) experiments.
Long-Term Visitation Value for Deep Exploration in Sparse Reward Reinforcement Learning
Empirical results on classic and novel benchmarks show that the proposed approach outperforms existing methods in environments with sparse rewards, especially in the presence of rewards that create suboptimal modes of the objective function.
