40 citations · 128 across the 11 of their papers we have counts for
11 papers
Demonstrating Multi-Suction Item Picking at Scale via Multi-Modal Learning of Pick Success
Che Wang, Jeroen van Baar, Chaitanya Mitash +6
This work demonstrates how autonomously learning aspects of robotic operation from sparsely-labeled, real-world data of deployed, engineered solutions at industrial scale can provi…
Pre-training with Synthetic Data Helps Offline Reinforcement Learning
Zecheng Wang, Che Wang, Zixuan Dong +1
Recently, it has been shown that for offline deep reinforcement learning (DRL), pre-training Decision Transformer with a large language corpus can improve downstream performance (R…
Reinforcement Learning with Automated Auxiliary Loss Search
Tairan He, Yuge Zhang, Kan Ren +5
A good state representation is crucial to solving complicated reinforcement learning (RL) challenges. Many recent works focus on designing auxiliary losses for learning informative…
On the Convergence of Monte Carlo UCB for Random-Length Episodic MDPs
Zixuan Dong, Che Wang, Keith Ross
In reinforcement learning, Monte Carlo algorithms update the Q function by averaging the episodic returns. In the Monte Carlo UCB (MC-UCB) algorithm, the action taken in each state…
VRL3: A Data-Driven Framework for Visual Deep Reinforcement Learning
Che Wang, Xufang Luo, Keith Ross +1
We propose VRL3, a powerful data-driven framework with a simple design for solving challenging visual deep reinforcement learning (DRL) tasks. We analyze a number of major obstacle…
Aggressive Q-Learning with Ensembles: Achieving Both High Sample Efficiency and High Asymptotic Performance
Yanqiu Wu, Xinyue Chen, Che Wang +2
Recent advances in model-free deep reinforcement learning (DRL) show that simple model-free methods can be highly effective in challenging high-dimensional continuous control tasks…