12 citations · 50 across the 15 of their papers we have counts for
16 papers
Humanoid Loco-Manipulation With Discrete VLA Model
Wenxin Shao, Siqi Chai, Kun Li +4
Vision-language-action (VLA) models using discrete action tokens have proven effective for controling robotic arms on manipulation tasks. For a humanoid, however, the whole-body ac…
Robot Policy Learning with Temporal Optimal Transport Reward
Yuwei Fu, Haichao Zhang, Di Wu +2
Reward specification is one of the most tricky problems in Reinforcement Learning, which usually requires tedious hand engineering in practice. One promising approach to tackle thi…
FuRL: Visual-Language Models as Fuzzy Rewards for Reinforcement Learning
Yuwei Fu, Haichao Zhang, Di Wu +2
In this work, we investigate how to leverage pre-trained visual-language models (VLM) for online Reinforcement Learning (RL). In particular, we focus on sparse reward tasks with pr…
Imitation with Spatial-Temporal Heatmap: 2nd Place Solution for NuPlan Challenge
Yihan Hu, Kun Li, Pingyuan Liang +7
This paper presents our 2nd place solution for the NuPlan Challenge 2023. Autonomous driving in real-world scenarios is highly complex and uncertain. Achieving safe planning in the…
Efficient Multi-Task and Transfer Reinforcement Learning with Parameter-Compositional Framework
Lingfeng Sun, Haichao Zhang, Wei Xu +1
In this work, we investigate the potential of improving multi-task training and also leveraging it for transferring in the reinforcement learning setting. We identify several chall…
Text-guided Eyeglasses Manipulation with Spatial Constraints
Jiacheng Wang, Ping Liu, Jingen Liu +1
Virtual try-on of eyeglasses involves placing eyeglasses of different shapes and styles onto a face image without physically trying them on. While existing methods have shown impre…
Policy Expansion for Bridging Offline-to-Online Reinforcement Learning
Haichao Zhang, We Xu, Haonan Yu
Pre-training with offline data and online fine-tuning using reinforcement learning is a promising strategy for learning control policies by leveraging the best of both worlds in te…
PaCo: Parameter-Compositional Multi-Task Reinforcement Learning
Lingfeng Sun, Haichao Zhang, Wei Xu +1
The purpose of multi-task reinforcement learning (MTRL) is to train a single policy that can be applied to a set of different tasks. Sharing parameters allows us to take advantage…
Generative Planning for Temporally Coordinated Exploration in Reinforcement Learning
Haichao Zhang, Wei Xu, Haonan Yu
Standard model-free reinforcement learning algorithms optimize a policy that generates the action to be taken in the current time step in order to maximize expected future return.…
Do You Need the Entropy Reward (in Practice)?
Haonan Yu, Haichao Zhang, Wei Xu
Maximum entropy (MaxEnt) RL maximizes a combination of the original task reward and an entropy reward. It is believed that the regularization imposed by entropy, on both policy imp…
Towards Safe Reinforcement Learning with a Safety Editor Policy
Haonan Yu, Wei Xu, Haichao Zhang
We consider the safe reinforcement learning (RL) problem of maximizing utility with extremely low constraint violation rates. Assuming no prior knowledge or pre-training of the env…
Natural Language for Human-Robot Collaboration: Problems Beyond Language Grounding
Seth Pate, Wei Xu, Ziyi Yang +3
To enable robots to instruct humans in collaborations, we identify several aspects of language processing that are not commonly studied in this context. These include location, pla…
Feature Importance in a Deep Learning Climate Emulator
Wei Xu, Xihaier Luo, Yihui Ren +3
We present a study using a class of post-hoc local explanation methods i.e., feature importance methods for "understanding" a deep learning (DL) emulator of climate. Specifically,…
Dilated Convolution based CSI Feedback Compression for Massive MIMO Systems
Shunpu Tang, Junjuan Xia, Lisheng Fan +3
Although the frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) system can offer high spectral and energy efficiency, it requires to feedback the downlin…
Super Solutions of the Model RB
Guangyan Zhou, Wei Xu
The concept of super solution is a special type of generalized solutions with certain degree of robustness and stability. In this paper we consider the -super solutions of t…
TAAC: Temporally Abstract Actor-Critic for Continuous Control
Haonan Yu, Wei Xu, Haichao Zhang
We present temporally abstract actor-critic (TAAC), a simple but effective off-policy RL algorithm that incorporates closed-loop temporal abstraction into the actor-critic framewor…