activity
20232026
most citedSafety-aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving

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

collaborators

14 papers

cs.RO2026

Anticipatory Robot Goalkeeping via Monotone Optimal Stopping

Hao E. Zhang, Ruize Geng, Yisen Li +8

Robots engaged in fast physical interactions often need to act before the intent of another agent is fully known. Anticipatory goalkeeping illustrates this challenge. Waiting provi…

cs.RO2026

Learning Versatile Humanoid Manipulation with Touch Dreaming

Yaru Niu, Zhenlong Fang, Binghong Chen +8

Humanoid robots promise general-purpose assistance, yet real-world humanoid loco-manipulation remains challenging because it requires whole-body stability, end-effector dexterity,…

cs.RO2026

HALO: Learning Human-Robot Collaboration via Heterogeneous-Agent Lyapunov Policy Optimization

Hao Zhang, Yaru Niu, Yikai Wang +2

To improve generalization and resilience in human-robot collaboration (HRC), robots must contend with diverse combinations of human behaviors and contexts, motivating multi-agent r…

cs.RO2026

Dexterous Manipulation Policies from RGB Human Videos via 3D Hand-Object Trajectory Reconstruction

Hongyi Chen, Tony Dong, Tiancheng Wu +7

Multi-finger robotic hand manipulation and grasping are challenging due to the high-dimensional action space and the difficulty of acquiring large-scale training data. Existing app…

cs.AI2025

Unifying Agent Interaction and World Information for Multi-agent Coordination

Dongsu Lee, Daehee Lee, Yaru Niu +3

This work presents a novel representation learning framework, *interaction-world* latent (IWoL), to facilitate *team coordination* in multi-agent reinforcement learning (MARL). Bui…

cs.RO2025

Human2LocoMan: Learning Versatile Quadrupedal Manipulation with Human Pretraining

Yaru Niu, Yunzhe Zhang, Mingyang Yu +11

Quadrupedal robots have demonstrated impressive locomotion capabilities in complex environments, but equipping them with autonomous versatile manipulation skills in a scalable way…

cs.RO2025

QuietPaw: Learning Quadrupedal Locomotion with Versatile Noise Preference Alignment

Yuyou Zhang, Yihang Yao, Shiqi Liu +7

When operating at their full capacity, quadrupedal robots can produce loud footstep noise, which can be disruptive in human-centered environments like homes, offices, and hospitals…

cs.RO2024

Learning Multi-Agent Loco-Manipulation for Long-Horizon Quadrupedal Pushing

Yuming Feng, Chuye Hong, Yaru Niu +6

Recently, quadrupedal locomotion has achieved significant success, but their manipulation capabilities, particularly in handling large objects, remain limited, restricting their us…

cs.CV2024

The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition

Lingdong Kong, Shaoyuan Xie, Hanjiang Hu +88

In the realm of autonomous driving, robust perception under out-of-distribution conditions is paramount for the safe deployment of vehicles. Challenges such as adverse weather, sen…

cs.RO2024

LocoMan: Advancing Versatile Quadrupedal Dexterity with Lightweight Loco-Manipulators

Changyi Lin, Xingyu Liu, Yuxiang Yang +6

Quadrupedal robots have emerged as versatile agents capable of locomoting and manipulating in complex environments. Traditional designs typically rely on the robot's inherent body…

cs.RO2023★ 2 cited

Interpretable Reinforcement Learning for Robotics and Continuous Control

Rohan Paleja, Letian Chen, Yaru Niu +10

Interpretability in machine learning is critical for the safe deployment of learned policies across legally-regulated and safety-critical domains. While gradient-based approaches i…

cs.RO2023★ 21 cited

Safety-aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving

Haohong Lin, Wenhao Ding, Zuxin Liu +4

In the domain of autonomous driving, the offline Reinforcement Learning~(RL) approaches exhibit notable efficacy in addressing sequential decision-making problems from offline data…

cs.RO2023★ 3 cited

Creative Robot Tool Use with Large Language Models

Mengdi Xu, Peide Huang, Wenhao Yu +7

Tool use is a hallmark of advanced intelligence, exemplified in both animal behavior and robotic capabilities. This paper investigates the feasibility of imbuing robots with the ab…

cs.RO2023

COMPOSER: Scalable and Robust Modular Policies for Snake Robots

Yuyou Zhang, Yaru Niu, Xingyu Liu +1

Snake robots have showcased remarkable compliance and adaptability in their interaction with environments, mirroring the traits of their natural counterparts. While their hyper-red…