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Hongyi Chen

7 accepted papers

2026

High Fidelity Capture, Reconstruction, and Transfer of Human Demonstrations for Robot-Assisted Bathing

Arjun Lakshmipathy, Jonathan P. King, Ethan Zuo, Rohit Satishkumar, Hongyi Chen, Jeffrey Ichnowski +3
RSS 2026poster

Despite the demand for robots in high-value clinical tasks like bathing, contemporary systems still lack the safety and reliability required for complex, sustained physical interaction with humans. A key challenge hindering the development of such systems is that collecting, understanding, and effec…

Cited by 0Source ↗Scholar ↗
2025

PID-controlled Langevin Dynamics for Faster Sampling on Generative Models

Hongyi Chen, Jianhai Shu, Jingtao Ding, Yong Li, Xiao-Ping Zhang
NeurIPS 2025poster

Langevin dynamics sampling suffers from extremely low generation speed, fundamentally limited by numerous fine-grained iterations to converge to the target distribution. We introduce PID-controlled Langevin Dynamics (PIDLD), a novel sampling acceleration algorithm that reinterprets the sampling proc…

Cited by 0Source ↗code ↗Scholar ↗
2024

KOROL: Learning Visualizable Object Feature with Koopman Operator Rollout for Manipulation

Hongyi Chen, ABULIKEMU ABUDUWEILI, Aviral Agrawal, Yunhai Han, Harish Ravichandar, Changliu Liu +1
CoRL 2024poster

Learning dexterous manipulation skills presents significant challenges due to complex nonlinear dynamics that underlie the interactions between objects and multi-fingered hands. Koopman operators have emerged as a robust method for modeling such nonlinear dynamics within a linear framework. However,…

Cited by 5Source ↗code ↗Scholar ↗
2024

Social Physics Informed Diffusion Model for Crowd Simulation

Hongyi Chen, Jingtao Ding, Yong Li, Yue Wang, Xiao-Ping Zhang
AAAI 2024technical

Crowd simulation holds crucial applications in various domains, such as urban planning, architectural design, and traffic arrangement. In recent years, physics-informed machine learning methods have achieved state-of-the-art performance in crowd simulation but fail to model the heterogeneity and mul…

Cited by 12Source ↗PDF ↗code ↗Scholar ↗
2023

KGNv2: Separating Scale and Pose Prediction for Keypoint-Based 6-DoF Grasp Synthesis on RGB-D Input

Yiye Chen, Ruinian Xu, Yunzhi Lin, Hongyi Chen, Patricio A. Vela
IROS 2023poster

We propose an improved keypoint approach for 6-DoF grasp pose synthesis from RGB-D input. Keypoint-based grasp detection from image input demonstrated promising results in a previous study, where the visual information provided by color imagery compensates for noisy or imprecise depth measurements.…

Cited by 4Source ↗code ↗Scholar ↗
2023

Planning with Sequence Models through Iterative Energy Minimization

Hongyi Chen, Yilun Du, Yiye Chen, Joshua B. Tenenbaum, Patricio A. Vela
ICLR 2023poster

Recent works have shown that language modeling can be effectively used to train reinforcement learning (RL) policies. However, the success of applying existing language models to planning, in which we wish to obtain a trajectory of actions to reach some goal, is less straightforward. The typical aut…

Cited by 9Source ↗PDF ↗code ↗Scholar ↗
2022

SGL: Symbolic Goal Learning in a Hybrid, Modular Framework for Human Instruction Following

Ruinian Xu, Hongyi Chen, Yunzhi Lin, Patricio A. Vela
RA-L 2022

This paper investigates human instruction following for robotic manipulation via a hybrid, modular system with symbolic and connectionist elements. Symbolic methods build modular systems with semantic parsing and task planning modules for producing sequences of actions from natural language requests

Cited by 7Source ↗code ↗Scholar ↗