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Found 22 papers, 5 papers with code
A Distributional View on Multi-Objective Policy Optimization
Many real-world problems require trading off multiple competing objectives.
The Control Toolbox - An Open-Source C++ Library for Robotics, Optimal and Model Predictive Control
The CT was designed to solve large-scale optimal control and estimation problems efficiently and allows for online control of dynamic systems.
Robotics Optimization and Control
Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes
We study the problem of robotic stacking with objects of complex geometry.
Ranked #2 on
Skill Generalization
on RGB-Stacking
Learning by Playing - Solving Sparse Reward Tasks from Scratch
We propose Scheduled Auxiliary Control (SAC-X), a new learning paradigm in the context of Reinforcement Learning (RL).
A Family of Iterative Gauss-Newton Shooting Methods for Nonlinear Optimal Control
This paper introduces a family of iterative algorithms for unconstrained nonlinear optimal control.
Systems and Control Robotics Optimization and Control
Simultaneously Learning Vision and Feature-based Control Policies for Real-world Ball-in-a-Cup
We present a method for fast training of vision based control policies on real robots.
Whole-Body Nonlinear Model Predictive Control Through Contacts for Quadrupeds
In this work we present a whole-body Nonlinear Model Predictive Control approach for Rigid Body Systems subject to contacts.
Robotics
Automatic Differentiation of Rigid Body Dynamics for Optimal Control and Estimation
First, we show a Trajectory Optimization example for the quadrupedal robot HyQ, which employs auto-differentiation on the dynamics including a contact model.
Robotics
Compositional Transfer in Hierarchical Reinforcement Learning
The successful application of general reinforcement learning algorithms to real-world robotics applications is often limited by their high data requirements.
General Reinforcement Learning
Hierarchical Reinforcement Learning
+4
Imagined Value Gradients: Model-Based Policy Optimization with Transferable Latent Dynamics Models
Humans are masters at quickly learning many complex tasks, relying on an approximate understanding of the dynamics of their environments.
Model-based Reinforcement Learning
Reinforcement Learning (RL)
+2
Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics
In contrast, we propose to treat hybrid problems in their 'native' form by solving them with hybrid reinforcement learning, which optimizes for discrete and continuous actions simultaneously.
Keep Doing What Worked: Behavioral Modelling Priors for Offline Reinforcement Learning
In practice, however, standard off-policy algorithms fail in the batch setting for continuous control.
Data-efficient Hindsight Off-policy Option Learning
We introduce Hindsight Off-policy Options (HO2), a data-efficient option learning algorithm.
Towards General and Autonomous Learning of Core Skills: A Case Study in Locomotion
Modern Reinforcement Learning (RL) algorithms promise to solve difficult motor control problems directly from raw sensory inputs.
Keep Doing What Worked: Behavior Modelling Priors for Offline Reinforcement Learning
In practice, however, standard off-policy algorithms fail in the batch setting for continuous control.
A distributional view on multi objective policy optimization
Many real-world problems require trading off multiple competing objectives.
Success at any cost: value constrained model-free continuous control
We demonstrate the efficiency of our approach using a number of continuous control benchmark tasks as well as a realistic, energy-optimized quadruped locomotion task.
Imitate and Repurpose: Learning Reusable Robot Movement Skills From Human and Animal Behaviors
We investigate the use of prior knowledge of human and animal movement to learn reusable locomotion skills for real legged robots.
SkillS: Adaptive Skill Sequencing for Efficient Temporally-Extended Exploration
The ability to effectively reuse prior knowledge is a key requirement when building general and flexible Reinforcement Learning (RL) agents.
Barkour: Benchmarking Animal-level Agility with Quadruped Robots
In the second approach, we distill the specialist skills into a Transformer-based generalist locomotion policy, named Locomotion-Transformer, that can handle various terrains and adjust the robot's gait based on the perceived environment and robot states.
Towards practical reinforcement learning for tokamak magnetic control
Reinforcement learning (RL) has shown promising results for real-time control systems, including the domain of plasma magnetic control.
Real-World Fluid Directed Rigid Body Control via Deep Reinforcement Learning
Recent advances in real-world applications of reinforcement learning (RL) have relied on the ability to accurately simulate systems at scale.
