2 citations · 2 across the 14 of their papers we have counts for
19 papers
Contextual Credit Policy Optimization
Huiwen Han, Ziyao Zeng, Zihao Zhu +6
Group-based reinforcement learning enables large language model agents to learn from sparse outcome rewards in long-horizon tasks. Recent methods derive step-level credit by compar…
TouchScale: 500 Hours of Human Vision and Touch for Visual-Tactile Learning
Dayou Li, Hao Wang, Qianqian Yang +23
Large-scale egocentric human interaction data is becoming an important source of physical supervision for embodied learning, yet video alone leaves the contact and pressure that ch…
Dexterous Tactile World Model
Ziyao Zeng, Xiatao Sun, Hao Wang +6
World models for manipulation are typically trained from video, yet the events that determine how manipulation unfolds, such as making and releasing contact, are difficult to obser…
Decoupling Vision, Language, and Action for Efficient Multi-Task Robot Policies
Xiatao Sun, Chen Liang, Ziyao Zeng +5
Vision-Language-Action (VLA) policies commonly run Vision-Language Model (VLM) backbones with billions of parameters at every policy inference, which costs latency and energy. We r…
OpenLongTail: Generative Scaling of Long-Tail Driving Data
Lulin Liu, Nuo Chen, Yan Wang +15
Scaling robust driving policies is fundamentally bottlenecked by the scarcity of edge cases in curated datasets. While the real world continuously captures these critical events, s…
A Physics-Grounded Benchmark for Multi-Agent Dynamics in World Models
Nuo Chen, Lulin Liu, Zihao Li +12
Generative world models hold immense promise as scalable simulators for autonomous systems, particularly for synthesizing rare but safety-critical multi-agent interactions, such as…
Artificial Foveated Perception for Mitigating Shortcut Learning in Robotic Foundation Models
Xiatao Sun, Yuan Zhuang, Mateo Sanchez Lopez Negrete +9
Robotic foundation models still need task-specific fine-tuning before deployment, and the fine-tuned policies often break under modest changes in scene layout, lighting, or nearby…
UniTac: A Unified Multimodal Model for Cross-Sensor Tactile Understanding and Generation
Jiahang Tu, Fengyu Yang, Chenyang Ma +8
Unified multimodal models (UMMs) have shown great promise in integrating understanding and generation across diverse modalities. However, existing research rarely extends this para…
Benchmarking AI Agents for Addressing Scientific Challenges Across Scales
Tianyu Liu, Allen Xin Wang, Antonia Panescu +30
AI agents are increasingly being developed to accelerate scientific discovery, yet their practical capabilities in real research settings remain poorly understood. Existing benchma…
RuleSmith: Multi-Agent LLMs for Automated Game Balancing
Ziyao Zeng, Chen Liu, Tianyu Liu +11
Game balancing is a longstanding challenge requiring repeated playtesting, expert intuition, and extensive manual tuning. We introduce RuleSmith, a framework that couples multi-age…
Coffee: Controllable Diffusion Fine-tuning
Ziyao Zeng, Jingcheng Ni, Ruyi Liu +1
Text-to-image diffusion models can generate diverse content with flexible prompts, which makes them well-suited for customization through fine-tuning with a small amount of user-pr…
ETA: Energy-based Test-time Adaptation for Depth Completion
Younjoon Chung, Hyoungseob Park, Patrick Rim +7
We propose a method for test-time adaptation of pretrained depth completion models. Depth completion models, trained on some ``source'' data, often predict erroneous outputs when t…
ProtoDepth: Unsupervised Continual Depth Completion with Prototypes
Patrick Rim, Hyoungseob Park, S. Gangopadhyay +3
We present ProtoDepth, a novel prototype-based approach for continual learning of unsupervised depth completion, the multimodal 3D reconstruction task of predicting dense depth map…
HOMER: Homography-Based Efficient Multi-view 3D Object Removal
Jingcheng Ni, Weiguang Zhao, Daniel Wang +4
3D object removal is an important sub-task in 3D scene editing, with broad applications in scene understanding, augmented reality, and robotics. However, existing methods struggle…
RSA: Resolving Scale Ambiguities in Monocular Depth Estimators through Language Descriptions
Ziyao Zeng, Yangchao Wu, Hyoungseob Park +6
We propose a method for metric-scale monocular depth estimation. Inferring depth from a single image is an ill-posed problem due to the loss of scale from perspective projection du…
Iris: Integrating Language into Diffusion-based Monocular Depth Estimation
Ziyao Zeng, Jingcheng Ni, Daniel Wang +5
Traditional monocular depth estimation suffers from inherent ambiguity and visual nuisances. We demonstrate that language can enhance monocular depth estimation by providing an add…
NeuroBind: Towards Unified Multimodal Representations for Neural Signals
Fengyu Yang, Chao Feng, Daniel Wang +8
Understanding neural activity and information representation is crucial for advancing knowledge of brain function and cognition. Neural activity, measured through techniques like e…
WorDepth: Variational Language Prior for Monocular Depth Estimation
Ziyao Zeng, Daniel Wang, Fengyu Yang +6
Three-dimensional (3D) reconstruction from a single image is an ill-posed problem with inherent ambiguities, i.e. scale. Predicting a 3D scene from text description(s) is similarly…
Binding Touch to Everything: Learning Unified Multimodal Tactile Representations
Fengyu Yang, Chao Feng, Ziyang Chen +8
The ability to associate touch with other modalities has huge implications for humans and computational systems. However, multimodal learning with touch remains challenging due to…