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Found 11 papers, 7 papers with code
RoboScript: Code Generation for Free-Form Manipulation Tasks across Real and Simulation
To bridge this ``ideal-to-real'' gap, this paper presents \textbf{RobotScript}, a platform for 1) a deployable robot manipulation pipeline powered by code generation; and 2) a code generation benchmark for robot manipulation tasks in free-form natural language.
Generalizable Visual Reinforcement Learning with Segment Anything Model
Learning policies that can generalize to unseen environments is a fundamental challenge in visual reinforcement learning (RL).
DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization
To quantify this inactivity, we adopt dormant ratio as a metric to measure inactivity in the RL agent's network.
GenSim: Generating Robotic Simulation Tasks via Large Language Models
Collecting large amounts of real-world interaction data to train general robotic policies is often prohibitively expensive, thus motivating the use of simulation data.
RL-ViGen: A Reinforcement Learning Benchmark for Visual Generalization
Visual Reinforcement Learning (Visual RL), coupled with high-dimensional observations, has consistently confronted the long-standing challenge of out-of-distribution generalization.
Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning
Hence, we propose Pre-trained Image Encoder for Generalizable visual reinforcement learning (PIE-G), a simple yet effective framework that can generalize to the unseen visual scenarios in a zero-shot manner.
On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline
In this paper, we examine the effectiveness of pre-training for visuo-motor control tasks.
A Comprehensive Survey of Data Augmentation in Visual Reinforcement Learning
Visual reinforcement learning (RL), which makes decisions directly from high-dimensional visual inputs, has demonstrated significant potential in various domains.
Extraneousness-Aware Imitation Learning
To tackle this problem, we introduce Extraneousness-Aware Imitation Learning (EIL), a self-supervised approach that learns visuomotor policies from third-person demonstrations with extraneous subsequences.
USEEK: Unsupervised SE(3)-Equivariant 3D Keypoints for Generalizable Manipulation
Can a robot manipulate intra-category unseen objects in arbitrary poses with the help of a mere demonstration of grasping pose on a single object instance?
Don't Touch What Matters: Task-Aware Lipschitz Data Augmentation for Visual Reinforcement Learning
One of the key challenges in visual Reinforcement Learning (RL) is to learn policies that can generalize to unseen environments.
