17 papers
ZOIF: Zeroth-Order Influence Function Estimation for LLM Data Selection Using Only Forward Passes
Lisong He, Yunxuan Jiang, Haishan Ye +1
Influence functions enable principled data selection for large language models (e.g., identifying influential training samples for model editing, debugging, or efficient fine-tunin…
F4R: Failure-Driven Recognition, Reconstruction, Refinement, and Redeployment for Continual Robot Self-Improvement
Zhuoyuan Yu, Jiacheng Wang, Tianle Liu +13
The real-world performance of current vision-language-action models is fundamentally constrained by the limited coverage of expert demonstrations and their insufficient understandi…
Learning Physical Interaction: A Survey of Tactile- and Force-aware Robot Learning
Shilin Shan, Chuhao Zhou, Ruize Wang +30
Physically grounded robot intelligence requires robots to perceive, reason about, and regulate their interactions with the physical world. This capability is particularly critical…
FuncBridge: Towards Functional Tool-Use Generalization via Keypoint Trajectory Reasoning
Chuhao Zhou, Liquan Wang, Shuxin Cao +5
While humans readily repurpose a book, a stone, or a shoe to drive a nail, robots trained on specific tools fail to transfer the same function to novel ones -- a gap we formalize a…
Conformal Path Reasoning: Trustworthy Knowledge Graph Question Answering via Path-Level Calibration
Shuhang Lin, Chuhao Zhou, Xiao Lin +5
Knowledge Graph Question Answering (KGQA) offers grounded, interpretable reasoning, but existing methods often fail to provide reliable coverage guarantees over retrieved answers.…
Rethinking Implicit Spatial Representation in Visuomotor Policy Learning
Xiangyu Chen, Yuxuan Hu, Chuhao Zhou +1
Generative model-based imitation learning has become a widely adopted paradigm for robotic manipulation, where policy performance depends critically on the conditioned visual repre…
Gaze2Act: Gaze-Conditioned Vision-Language-Action Policies for Interactive Robot Manipulation
Kuangji Zuo, Gen Li, Bofan Lyu +9
Vision-Language-Action (VLA) models have recently shown strong potential for robot learning by following language instructions. However, in practice, language alone is often insuff…
CompassAD: Intent-Driven 3D Affordance Grounding in Functionally Competing Objects
Jingliang Li, Jindou Jia, Tuo An +7
When told to "cut the cake," a robot must choose the knife over nearby scissors, despite both objects affording the same cutting function. In real-world scenes, multiple objects ma…
Feedback World Model Enables Precise Guidance of Diffusion Policy
Tuo An, Jindou Jia, Gen Li +8
World models aim to improve robotic decision making by predicting the consequences of actions. However, in practice, their predictions often become unreliable once the robot encoun…
Trace-Focused Diffusion Policy for Multi-Modal Action Disambiguation in Long-Horizon Robotic Manipulation
Yuxuan Hu, Xiangyu Chen, Chuhao Zhou +4
Generative model-based policies have shown strong performance in imitation-based robotic manipulation by learning action distributions from demonstrations. However, in long-horizon…
When Robots Should Say "I Don't Know": Benchmarking Abstention in Embodied Question Answering
Tao Wu, Chuhao Zhou, Guangyu Zhao +3
Embodied Question Answering (EQA) requires an agent to interpret language, perceive its environment, and navigate within 3D scenes to produce responses. Existing EQA benchmarks ass…
Policy: Mutable Material Manipulation Augmentation Policy through Photometric Re-rendering
Jiayi Li, Yuxuan Hu, Haoran Geng +4
Material generalization is essential for real-world robotic manipulation, where robots must interact with objects exhibiting diverse visual and physical properties. This challenge…
RM-RL: Role-Model Reinforcement Learning for Precise Robot Manipulation
Xiangyu Chen, Chuhao Zhou, Yuxi Liu +1
Precise robot manipulation is critical for fine-grained applications such as chemical and biological experiments, where even small errors (e.g., reagent spillage) can invalidate an…
HoloLLM: Multisensory Foundation Model for Language-Grounded Human Sensing and Reasoning
Chuhao Zhou, Jianfei Yang
Embodied agents operating in smart homes must understand human behavior through diverse sensory inputs and communicate via natural language. While Vision-Language Models (VLMs) hav…
REI-Bench: Can Embodied Agents Understand Vague Human Instructions in Task Planning?
Chenxi Jiang, Chuhao Zhou, Jianfei Yang
Robot task planning decomposes human instructions into executable action sequences that enable robots to complete a series of complex tasks. Although recent large language model (L…
NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries
Tao Wu, Chuhao Zhou, Yen Heng Wong +2
The rapid advancement of Vision-Language Models (VLMs) has significantly advanced the development of Embodied Question Answering (EQA), enhancing agents' abilities in language unde…
Enhancing Dataset Distillation via Label Inconsistency Elimination and Learning Pattern Refinement
Chuhao Zhou, Chenxi Jiang, Yi Xie +2
Dataset Distillation (DD) seeks to create a condensed dataset that, when used to train a model, enables the model to achieve performance similar to that of a model trained on the e…