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Haonan Duan

14 accepted papers

2026

DiffusionHandover: Reliable Human-to-Robot Handover Generation With Anthropomorphic Hand

RA-L 2026

Human-to-robot handover is a fundamental capability in human-robot interaction, critical for effective collaboration in service and assistive domains. Despite recent progress, ensuring both reliability and safety-particularly collision-free interaction with the human hand-remains a major challenge,

Cited by 0SourceScholar
2026

EVA: Efficient Reinforcement Learning for End-to-End Video Agent

CVPR 2026

Video understanding with multimodal large language models (MLLMs) remains challenging due to the long token sequences of videos, which contain extensive temporal dependencies and redundant frames.Existing approaches typically treat MLLMs as passive recognizers, processing entire videos or uniformly

Cited by 0SourcecodeScholar
2026

Grounding Actions in Camera Space: Observation-Centric Vision-Language-Action Policy

AAAI 2026technical

Vision-Language-Action (VLA) models frequently encounter challenges in generalizing to real-world environments due to inherent discrepancies between observation and action spaces. Although training data are collected from diverse camera perspectives, the models typically predict end-effector poses w

Cited by 0SourcePDFScholar
2026

ScaleADFG: Affordance-Based Dexterous Functional Grasping via Scalable Dataset

RA-L 2026

Dexterous functional tool-use grasping is essential for effective robotic manipulation of tools. However, existing approaches face significant challenges in efficiently constructing large-scale datasets and ensuring generalizability to everyday object scales. These issues primarily arise from size m

Cited by 1SourcecodeScholar
2025

BioReason: Incentivizing Multimodal Biological Reasoning within a DNA-LLM Model

NeurIPS 2025poster

Unlocking deep and interpretable biological reasoning from complex genomic data remains a major AI challenge limiting scientific progress. While current DNA foundation models excel at representing sequences, they struggle with multi-step reasoning and lack transparent, biologically meaningful explan…

Cited by 0SourcecodeScholar
2025

Dita: Scaling Diffusion Transformer for Generalist Vision-Language-Action Policy

ICCV 2025poster

While recent vision-language-action models trained on diverse robot datasets exhibit promising generalization capabilities with limited in-domain data, their reliance on compact action heads to predict discretized or continuous actions constrains adaptability to heterogeneous action spaces. We prese…

Cited by 0SourcePDFScholar
2025

GraspAgent 1.0: Adversarial Continual Dexterous Grasp Learning

RA-L 2025

Grasp is at the core of robotic manipulation tasks. Nonetheless, most 6-DOF methods resort to a one-time setup via intensive analytics and targeting a predetermined domain. On the other hand, learning and adapting in real environments is of great promise to robotics yet challenging. In this context,

Cited by 0SourceScholar
2025

Measuring Scientific Capabilities of Language Models with a Systems Biology Dry Lab

NeurIPS 2025poster

Designing experiments and result interpretations are core scientific competencies, particularly in biology, where researchers perturb complex systems to uncover the underlying systems. Recent efforts to evaluate the scientific capabilities of large language models (LLMs) fail to test these competenc…

Cited by 0SourceScholar
2024

Learning Realistic and Reasonable Grasps for Anthropomorphic Hand in Cluttered Scenes

ICRA 2024poster

Grasping is one of the most fundamental skills for humans to interact with objects. However, it remains a challenging problem for anthropomorphic hands, due to the lack of object affordance understanding and high-dimensional grasp planning. In this work, we propose an anthropomorphic hand grasping f…

Cited by 2SourceScholar
2023

Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models

NeurIPS 2023poster

Large language models (LLMs) are excellent in-context learners. However, the sensitivity of data contained in prompts raises privacy concerns. Our work first shows that these concerns are valid: we instantiate a simple but highly effective membership inference attack against the data used to prompt…

Cited by 86SourcePDFScholar
2022

Augment with Care: Contrastive Learning for Combinatorial Problems

ICML 2022spotlight

Supervised learning can improve the design of state-of-the-art solvers for combinatorial problems, but labelling large numbers of combinatorial instances is often impractical due to exponential worst-case complexity. Inspired by the recent success of contrastive pre-training for images, we conduct a…

2022

Dataset Inference for Self-Supervised Models

NeurIPS 2022accept

Self-supervised models are increasingly prevalent in machine learning (ML) since they reduce the need for expensively labeled data. Because of their versatility in downstream applications, they are increasingly used as a service exposed via public APIs. At the same time, these encoder models are par…

Cited by 36SourcePDFScholar
2022

Distributional Reinforcement Learning with Monotonic Splines

ICLR 2022poster

Distributional Reinforcement Learning (RL) differs from traditional RL by estimating the distribution over returns to capture the intrinsic uncertainty of MDPs. One key challenge in distributional RL lies in how to parameterize the quantile function when minimizing the Wasserstein metric of temporal…

Cited by 21SourcePDFScholar
2020

Online Bayesian Moment Matching based SAT Solver Heuristics

ICML 2020poster

In this paper, we present a Bayesian Moment Matching (BMM) based method aimed at solving the initialization problem in Boolean SAT solvers. The initialization problem can be stated as follows: given a SAT formula $\phi$, compute an initial order over the variables of $\phi$ and values/polarity for t…