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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,982 papers · 148 categories

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6 results for “sim2real”

Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RLcs.LG

This work shows how to use simulators to learn efficient exploration in real-world RL.

problem Sample complexity of real-world reinforcement learning.

method Coupling exploratory policies learned in simulators with practical approaches.

result Polynomial sample complexity in real world, exponential improvement over direct sim2real transfer.

reinforcement learning sim-to-real exploration sample complexity low-rank MDPs

2024-10-26abs ↗pdf ↗

Learning to Augment Synthetic Images for Sim2Real Policy Transfercs.LG

Optimizes synthetic image augmentation for sim2real policy transfer in robotics.

problem Difficulty in transferring learned policies from simulated to real environments.

method Optimizes random transformations to augment synthetic images, enabling policy learning without real data.

result Significant improvement in policy accuracy on real robots for three manipulation tasks.

policy transfer sim2real domain randomization augmentation manipulation tasks

2019-03-18abs ↗pdf ↗

Continual Reinforcement Learning deployed in Real-life using Policy Distillation and Sim2Real Transfercs.LG

Robot learns new tasks sequentially without forgetting past ones.

problem Teaching a robot to solve tasks in a continual learning scenario.

method Policy distillation and sim2real transfer.

result Robot can solve all encountered tasks without forgetting past ones.

continual reinforcement learning robotics policy distillation sim2real transfer sample efficiency

2019-06-11abs ↗pdf ↗

Solving Rubik's Cube with a Robot Handcs.LG

Robot solves Rubik's cube using simulation-trained models.

problem Solving Rubik's cube with a robot hand.

method Automatic domain randomization (ADR) and a custom robot platform.

result Control policies and vision state estimators trained with ADR exhibit improved sim2real transfer.

Rubik's cube robotics machine learning automatic domain randomization meta-learning

2019-10-16abs ↗pdf ↗

Curriculum in Gradient-Based Meta-Reinforcement Learningcs.LG

Gradient-based meta-RL fails with incorrect task distributions, leading to instability and poor performance.

problem Gradient-based meta-RL's sensitivity to task distributions causes instability and poor performance.

method Proposes meta Active Domain Randomization (meta-ADR) to learn task distributions for gradient-based meta-RL.

result Meta-ADR improves stability and generalization of MAML on simulated locomotion and navigation tasks.

meta-reinforcement learning gradient-based meta-learning task distribution domain randomization meta-overfitting

2020-02-19abs ↗pdf ↗

TACTO: A Fast, Flexible, and Open-source Simulator for High-Resolution Vision-based Tactile Sensorscs.RO

TACTO simulates high-resolution touch sensing for robotics.

problem Accurate simulation of touch sensing in robotics.

method Fast, flexible, open-source simulator for vision-based tactile sensors.

result Demonstrated TACTO's effectiveness in grasping stability prediction and marble manipulation control.

robotics tactile sensing vision-based sensors simulators machine learning

2020-12-15abs ↗pdf ↗