Deep reinforcement learning for robotics: A survey of real-world successes
Reinforcement learning (RL), particularly its combination with deep neural networks,
referred to as deep RL (DRL), has shown tremendous promise across a wide range of …
referred to as deep RL (DRL), has shown tremendous promise across a wide range of …
[HTML][HTML] A review on reinforcement learning for contact-rich robotic manipulation tasks
Í Elguea-Aguinaco, A Serrano-Muñoz… - Robotics and Computer …, 2023 - Elsevier
Research and application of reinforcement learning in robotics for contact-rich manipulation
tasks have exploded in recent years. Its ability to cope with unstructured environments and …
tasks have exploded in recent years. Its ability to cope with unstructured environments and …
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 …
Design and experimental validation of deep reinforcement learning-based fast trajectory planning and control for mobile robot in unknown environment
This article is concerned with the problem of planning optimal maneuver trajectories and
guiding the mobile robot toward target positions in uncertain environments for exploration …
guiding the mobile robot toward target positions in uncertain environments for exploration …
Behavior: Benchmark for everyday household activities in virtual, interactive, and ecological environments
We introduce BEHAVIOR, a benchmark for embodied AI with 100 activities in simulation,
spanning a range of everyday household chores such as cleaning, maintenance, and food …
spanning a range of everyday household chores such as cleaning, maintenance, and food …
Why generalization in rl is difficult: Epistemic pomdps and implicit partial observability
Generalization is a central challenge for the deployment of reinforcement learning (RL)
systems in the real world. In this paper, we show that the sequential structure of the RL …
systems in the real world. In this paper, we show that the sequential structure of the RL …
igibson 1.0: A simulation environment for interactive tasks in large realistic scenes
We present iGibson 1.0, a novel simulation environment to develop robotic solutions for
interactive tasks in large-scale realistic scenes. Our environment contains 15 fully interactive …
interactive tasks in large-scale realistic scenes. Our environment contains 15 fully interactive …
Interactive gibson benchmark: A benchmark for interactive navigation in cluttered environments
We present Interactive Gibson Benchmark, the first comprehensive benchmark for training
and evaluating Interactive Navigation solutions. Interactive Navigation tasks are robot …
and evaluating Interactive Navigation solutions. Interactive Navigation tasks are robot …
The ingredients of real-world robotic reinforcement learning
The success of reinforcement learning for real world robotics has been, in many cases
limited to instrumented laboratory scenarios, often requiring arduous human effort and …
limited to instrumented laboratory scenarios, often requiring arduous human effort and …
Combining optimal control and learning for visual navigation in novel environments
Abstract Model-based control is a popular paradigm for robot navigation because it can
leverage a known dynamics model to efficiently plan robust robot trajectories. However, it is …
leverage a known dynamics model to efficiently plan robust robot trajectories. However, it is …