Crossing the reality gap: A survey on sim-to-real transferability of robot controllers in reinforcement learning
The growing demand for robots able to act autonomously in complex scenarios has widely
accelerated the introduction of Reinforcement Learning (RL) in robots control applications …
accelerated the introduction of Reinforcement Learning (RL) in robots control applications …
Robot learning from randomized simulations: A review
The rise of deep learning has caused a paradigm shift in robotics research, favoring
methods that require large amounts of data. Unfortunately, it is prohibitively expensive to …
methods that require large amounts of data. Unfortunately, it is prohibitively expensive to …
Epopt: Learning robust neural network policies using model ensembles
Sample complexity and safety are major challenges when learning policies with
reinforcement learning for real-world tasks, especially when the policies are represented …
reinforcement learning for real-world tasks, especially when the policies are represented …
Sfv: Reinforcement learning of physical skills from videos
Data-driven character animation based on motion capture can produce highly naturalistic
behaviors and, when combined with physics simulation, can provide for natural procedural …
behaviors and, when combined with physics simulation, can provide for natural procedural …
Mujoco: A physics engine for model-based control
We describe a new physics engine tailored to model-based control. Multi-joint dynamics are
represented in generalized coordinates and computed via recursive algorithms. Contact …
represented in generalized coordinates and computed via recursive algorithms. Contact …
Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation
Modeling human motor control and predicting how humans will move in novel environments
is a grand scientific challenge. Researchers in the fields of biomechanics and motor control …
is a grand scientific challenge. Researchers in the fields of biomechanics and motor control …
Learning predict-and-simulate policies from unorganized human motion data
The goal of this research is to create physically simulated biped characters equipped with a
rich repertoire of motor skills. The user can control the characters interactively by modulating …
rich repertoire of motor skills. The user can control the characters interactively by modulating …
Learning basketball dribbling skills using trajectory optimization and deep reinforcement learning
Basketball is one of the world's most popular sports because of the agility and speed
demonstrated by the players. This agility and speed makes designing controllers to realize …
demonstrated by the players. This agility and speed makes designing controllers to realize …
Flexible muscle-based locomotion for bipedal creatures
T Geijtenbeek, M Van De Panne… - ACM Transactions on …, 2013 - dl.acm.org
We present a muscle-based control method for simulated bipeds in which both the muscle
routing and control parameters are optimized. This yields a generic locomotion control …
routing and control parameters are optimized. This yields a generic locomotion control …
Optimizing locomotion controllers using biologically-based actuators and objectives
We present a technique for automatically synthesizing walking and running controllers for
physically-simulated 3D humanoid characters. The sagittal hip, knee, and ankle degrees-of …
physically-simulated 3D humanoid characters. The sagittal hip, knee, and ankle degrees-of …