86 citations · 90 across the 5 of their papers we have counts for
6 papers
When One Step Is Enough: An Empirical Study of Euler Integration in Flow-Matching Policies
Shivakanth Sujit, Luca Nunziante, Rousslan Fernand Julien Dossa +1
Flow matching is now the dominant approach for vision-language action (VLA) robot policies, thanks to its expressiveness and simple training objective. However, inference for stand…
An Augmented Reality Brain-Robot Interface for Generalist Robot Arm Manipulation
Shangkai Zhang, Rousslan Fernand Julien Dossa, Luca Nunziante +2
The integration of augmented reality (AR) and EEG-based brain-computer interfaces (BCIs) offers a promising path for enabling intuitive control of robots for assistive purposes. Ho…
Improving Low-Cost Teleoperation: Augmenting GELLO with Force
Shivakanth Sujit, Luca Nunziante, Dan Ogawa Lillrank +2
In this work we extend the low-cost GELLO teleoperation system, initially designed for joint position control, with additional force information. Our first extension is to implemen…
Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning
Shengyi Huang, Quentin Gallouédec, Florian Felten +30
In many Reinforcement Learning (RL) papers, learning curves are useful indicators to measure the effectiveness of RL algorithms. However, the complete raw data of the learning curv…
A2C is a special case of PPO
Shengyi Huang, Anssi Kanervisto, Antonin Raffin +3
Advantage Actor-critic (A2C) and Proximal Policy Optimization (PPO) are popular deep reinforcement learning algorithms used for game AI in recent years. A common understanding is t…
CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms
Shengyi Huang, Rousslan Fernand Julien Dossa, Chang Ye +1
CleanRL is an open-source library that provides high-quality single-file implementations of Deep Reinforcement Learning algorithms. It provides a simpler yet scalable developing ex…