activity
20212023
most citedFoldsformer: Learning Sequential Multi-Step Cloth Manipulation With Space-Time Attention

26 citations · 76 across the 14 of their papers we have counts for

collaborators

23 papers

cs.AI2024

DeepMF: Deep Motion Factorization for Closed-Loop Safety-Critical Driving Scenario Simulation

Yizhe Li, Linrui Zhang, Xueqian Wang +2

Safety-critical traffic scenarios are of great practical relevance to evaluating the robustness of autonomous driving (AD) systems. Given that these long-tail events are extremely…

cs.RO2024

ViIK: Flow-based Vision Inverse Kinematics Solver with Fusing Collision Checking

Qinglong Meng, Chongkun Xia, Xueqian Wang

Inverse Kinematics (IK) is to find the robot's configurations that satisfy the target pose of the end effector. In motion planning, diverse configurations were required in case a f…

cs.CR20241 cited

Probing the Safety Response Boundary of Large Language Models via Unsafe Decoding Path Generation

Haoyu Wang, Bingzhe Wu, Yatao Bian +3

Large Language Models (LLMs) are implicit troublemakers. While they provide valuable insights and assist in problem-solving, they can also potentially serve as a resource for malic…

cs.RO20242 cited

LVCP: LiDAR-Vision Tightly Coupled Collaborative Real-time Relative Positioning

Zhuozhu Jian, Qixuan Li, Shengtao Zheng +2

In air-ground collaboration scenarios without GPS and prior maps, the relative positioning of drones and unmanned ground vehicles (UGVs) has always been a challenge. For a drone eq…

cs.LG20242 cited

DEER: A Delay-Resilient Framework for Reinforcement Learning with Variable Delays

Bo Xia, Yilun Kong, Yongzhe Chang +4

Classic reinforcement learning (RL) frequently confronts challenges in tasks involving delays, which cause a mismatch between received observations and subsequent actions, thereby…

cs.CV2024

Heterogeneous Federated Learning with Splited Language Model

Yifan Shi, Yuhui Zhang, Ziyue Huang +4

Federated Split Learning (FSL) is a promising distributed learning paradigm in practice, which gathers the strengths of both Federated Learning (FL) and Split Learning (SL) paradig…