4 citations · 5 across the 17 of their papers we have counts for
18 papers
One from Infinity: Actualizing Futures from Pretrained World Models into Robot Actions
Bang Du, Yichen Xie, Shuqi Zhao +3
A pretrained video world model admits many plausible futures for a scene, but a robot must realize the exact task-conditioned one. To turn world models into executable robot polici…
TANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action Model
Anqi Li, Yuxin Chen, Zhaobo Li +4
We study the problem of navigating cluttered indoor environments with a humanoid robot. Unlike conventional methods that model navigation as a 2D path planning problem, humanoid tr…
CLIFT: Turning Gemini Robotics On-Device into Humanoid Specialists via Non-Invasive Closed-Loop Iterative Fine-Tuning
Yuxin Chen, Hari Srikanth, Nathan Jew +7
While robot foundation models are growing increasingly capable, the strongest models are typically trained on proprietary data and remain closed-source, limiting downstream users'…
REAR: Test-time Preference Realignment through Reward Decomposition
Fuxiang Zhang, Pengcheng Wang, Chenran Li +6
Aligning large language models (LLMs) with diverse user preferences is a critical yet challenging task. While post-training methods can adapt models to specific needs, they often r…
Learning to Trigger: Reinforcement Learning at the Large Hadron Collider
Zixin Ding, Shaghayegh Emami, Giovanna Salvi +7
High-throughput scientific facilities such as the Large Hadron Collider depend on real-time event filtering (\textit{triggering}) under tight constraints on bandwidth, latency, and…
SidConArena: An Environment Evaluating Agents in Open-Ended,Positive-Sum Bargaining Game
Yeqi Feng, Yuxin Chen, Tianxing He
Evaluating LLM agents requires dynamic environments that go beyond static reasoning and zero-sum games. Real-world economic interaction is often open-ended and mixed-motive: agents…
TEXEDO : Test Time Scaling for Controller-aware Language-conditioned Humanoid Motion Generation
Jianuo Cao, Yuxin Chen, Yuzhen Song +3
Text-conditioned motion generation is a promising interface for programming humanoid robots, yet current generators are often trained on human motion datasets retargeted to robot m…
Rethinking Image-to-3D Generation with Sparse Queries: Efficiency, Capacity, and Input-View Bias
Zhiyuan Xu, Jiuming Liu, Yuxin Chen +3
We present SparseGen, a novel framework for efficient image-to-3D generation, which exhibits low input-view bias while being significantly faster. Unlike traditional approaches tha…
Mean Flow Policy with Instantaneous Velocity Constraint for One-step Action Generation
Guojian Zhan, Letian Tao, Pengcheng Wang +6
Learning expressive and efficient policy functions is a promising direction in reinforcement learning (RL). While flow-based policies have recently proven effective in modeling com…
Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control
Yuxin Chen, Jianglan Wei, Chenfeng Xu +4
World models enable robots to "imagine" future observations given current observations and planned actions, and have been increasingly adopted as generalized dynamics models to fac…
DexCtrl: Towards Sim-to-Real Dexterity with Adaptive Controller Learning
Shuqi Zhao, Ke Yang, Yuxin Chen +5
Dexterous manipulation has seen remarkable progress in recent years, with policies capable of executing many complex and contact-rich tasks in simulation. However, transferring the…
Residual Policy Gradient: A Reward View of KL-regularized Objective
Pengcheng Wang, Xinghao Zhu, Yuxin Chen +3
Reinforcement Learning and Imitation Learning have achieved widespread success in many domains but remain constrained during real-world deployment. One of the main issues is the ad…
Physics-Aware Robotic Palletization with Online Masking Inference
Tianqi Zhang, Zheng Wu, Yuxin Chen +6
The efficient planning of stacking boxes, especially in the online setting where the sequence of item arrivals is unpredictable, remains a critical challenge in modern warehouse an…
FDPP: Fine-tune Diffusion Policy with Human Preference
Yuxin Chen, Devesh K. Jha, Masayoshi Tomizuka +1
Imitation learning from human demonstrations enables robots to perform complex manipulation tasks and has recently witnessed huge success. However, these techniques often struggle…
Testing Human-Hand Segmentation on In-Distribution and Out-of-Distribution Data in Human-Robot Interactions Using a Deep Ensemble Model
Reza Jalayer, Yuxin Chen, Masoud Jalayer +2
Reliable detection and segmentation of human hands are critical for enhancing safety and facilitating advanced interactions in human-robot collaboration. Current research predomina…
DexH2R: Task-oriented Dexterous Manipulation from Human to Robots
Shuqi Zhao, Xinghao Zhu, Yuxin Chen +5
Dexterous manipulation is a critical aspect of human capability, enabling interaction with a wide variety of objects. Recent advancements in learning from human demonstrations and…
MEReQ: Max-Ent Residual-Q Inverse RL for Sample-Efficient Alignment from Intervention
Yuxin Chen, Chen Tang, Jianglan Wei +6
Aligning robot behavior with human preferences is crucial for deploying embodied AI agents in human-centered environments. A promising solution is interactive imitation learning fr…
Quantifying Agent Interaction in Multi-agent Reinforcement Learning for Cost-efficient Generalization
Yuxin Chen, Chen Tang, Ran Tian +4
Generalization poses a significant challenge in Multi-agent Reinforcement Learning (MARL). The extent to which an agent is influenced by unseen co-players depends on the agent's po…