19 citations · 41 across the 10 of their papers we have counts for
10 papers
T-Rex: Tactile-Reactive Dexterous Manipulation
Dantong Niu, Zhuoyang Liu, Zekai Wang +31
The ability to react dynamically to tactile signals has long been considered crucial to agile human-level dexterity. Yet contemporary learning-based Vision-Language-Action (VLA) mo…
DexUMI: Using Human Hand as the Universal Manipulation Interface for Dexterous Manipulation
Mengda Xu, Han Zhang, Yifan Hou +4
We present DexUMI - a data collection and policy learning framework that uses the human hand as the natural interface to transfer dexterous manipulation skills to various robot han…
Flow as the Cross-Domain Manipulation Interface
Mengda Xu, Zhenjia Xu, Yinghao Xu +4
We present Im2Flow2Act, a scalable learning framework that enables robots to acquire real-world manipulation skills without the need of real-world robot training data. The key idea…
O3D: Offline Data-driven Discovery and Distillation for Sequential Decision-Making with Large Language Models
Yuchen Xiao, Yanchao Sun, Mengda Xu +4
Recent advancements in large language models (LLMs) have exhibited promising performance in solving sequential decision-making problems. By imitating few-shot examples provided in…
XSkill: Cross Embodiment Skill Discovery
Mengda Xu, Zhenjia Xu, Cheng Chi +2
Human demonstration videos are a widely available data source for robot learning and an intuitive user interface for expressing desired behavior. However, directly extracting reusa…
Towards Multi-Agent Reinforcement Learning driven Over-The-Counter Market Simulations
Nelson Vadori, Leo Ardon, Sumitra Ganesh +7
We study a game between liquidity provider and liquidity taker agents interacting in an over-the-counter market, for which the typical example is foreign exchange. We show how a su…
ASPiRe:Adaptive Skill Priors for Reinforcement Learning
Mengda Xu, Manuela Veloso, Shuran Song
We introduce ASPiRe (Adaptive Skill Prior for RL), a new approach that leverages prior experience to accelerate reinforcement learning. Unlike existing methods that learn a single…
Mixture of basis for interpretable continual learning with distribution shifts
Mengda Xu, Sumitra Ganesh, Pranay Pasula
Continual learning in environments with shifting data distributions is a challenging problem with several real-world applications. In this paper we consider settings in which the d…
Towards a fully RL-based Market Simulator
Leo Ardon, Nelson Vadori, Thomas Spooner +3
We present a new financial framework where two families of RL-based agents representing the Liquidity Providers and Liquidity Takers learn simultaneously to satisfy their objective…
Reinforcement Learning for Market Making in a Multi-agent Dealer Market
Sumitra Ganesh, Nelson Vadori, Mengda Xu +3
Market makers play an important role in providing liquidity to markets by continuously quoting prices at which they are willing to buy and sell, and managing inventory risk. In thi…