51 citations · 125 across the 9 of their papers we have counts for
9 papers
SayTap: Language to Quadrupedal Locomotion
Yujin Tang, Wenhao Yu, Jie Tan +3
Large language models (LLMs) have demonstrated the potential to perform high-level planning. Yet, it remains a challenge for LLMs to comprehend low-level commands, such as joint an…
DEIR: Efficient and Robust Exploration through Discriminative-Model-Based Episodic Intrinsic Rewards
Shanchuan Wan, Yujin Tang, Yingtao Tian +1
Exploration is a fundamental aspect of reinforcement learning (RL), and its effectiveness is a deciding factor in the performance of RL algorithms, especially when facing sparse ex…
Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter
Aleksandar Stanić, Yujin Tang, David Ha +1
Reinforcement learning agents must generalize beyond their training experience. Prior work has focused mostly on identical training and evaluation environments. Starting from the r…
Learning to Imagine: Diversify Memory for Incremental Learning using Unlabeled Data
Yu-Ming Tang, Yi-Xing Peng, Wei-Shi Zheng
Deep neural network (DNN) suffers from catastrophic forgetting when learning incrementally, which greatly limits its applications. Although maintaining a handful of samples (called…
Evolving Modular Soft Robots without Explicit Inter-Module Communication using Local Self-Attention
Federico Pigozzi, Yujin Tang, Eric Medvet +1
Modularity in robotics holds great potential. In principle, modular robots can be disassembled and reassembled in different robots, and possibly perform new tasks. Nevertheless, ac…
EvoJAX: Hardware-Accelerated Neuroevolution
Yujin Tang, Yingtao Tian, David Ha
Evolutionary computation has been shown to be a highly effective method for training neural networks, particularly when employed at scale on CPU clusters. Recent work have also sho…
The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning
Yujin Tang, David Ha
In complex systems, we often observe complex global behavior emerge from a collection of agents interacting with each other in their environment, with each individual agent acting…
Learning Agile Locomotion via Adversarial Training
Yujin Tang, Jie Tan, Tatsuya Harada
Developing controllers for agile locomotion is a long-standing challenge for legged robots. Reinforcement learning (RL) and Evolution Strategy (ES) hold the promise of automating t…
Neuroevolution of Self-Interpretable Agents
Yujin Tang, Duong Nguyen, David Ha
Inattentional blindness is the psychological phenomenon that causes one to miss things in plain sight. It is a consequence of the selective attention in perception that lets us rem…