Heng Zhang

Orcid: 0000-0003-4832-9668

Affiliations:
  • University of Genoa, Human-Robot Interfaces and Interaction Laboratory, Italy


According to our database1, Heng Zhang authored at least 15 papers between 2024 and 2026.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2026
Learning Tactile-Aware Quadrupedal Loco-Manipulation Policies.
CoRR, April, 2026

TacVLA: Contact-Aware Tactile Fusion for Robust Vision-Language-Action Manipulation.
CoRR, March, 2026

Reward-Zero: Language Embedding Driven Implicit Reward Mechanisms for Reinforcement Learning.
CoRR, March, 2026

Self-supervised Physics-Informed Manipulation of Deformable Linear Objects with Non-negligible Dynamics.
CoRR, February, 2026

AgenticLab: A Real-World Robot Agent Platform that Can See, Think, and Act.
CoRR, February, 2026

CompliantVLA-adaptor: VLM-Guided Variable Impedance Action for Safe Contact-Rich Manipulation.
CoRR, January, 2026

2025
Safe Learning for Contact-Rich Robot Tasks: A Survey from Classical Learning-Based Methods to Safe Foundation Models.
CoRR, December, 2025

OmniVIC: A Self-Improving Variable Impedance Controller with Vision-Language In-Context Learning for Safe Robotic Manipulation.
CoRR, October, 2025

ActivePose: Active 6D Object Pose Estimation and Tracking for Robotic Manipulation.
CoRR, September, 2025

Semantic Visual Simultaneous Localization and Mapping: A Survey.
IEEE Trans. Intell. Transp. Syst., June, 2025

A Survey on Imitation Learning for Contact-Rich Tasks in Robotics.
CoRR, June, 2025

Scaling Laws of Scientific Discovery with AI and Robot Scientists.
CoRR, March, 2025

Bresa: Bio-inspired Reflexive Safe Reinforcement Learning for Contact-Rich Robotic Tasks.
CoRR, March, 2025

Towards Passive Safe Reinforcement Learning: A Comparative Study on Contact-rich Robotic Manipulation.
CoRR, March, 2025

2024
SRL-VIC: A Variable Stiffness-Based Safe Reinforcement Learning for Contact-Rich Robotic Tasks.
IEEE Robotics Autom. Lett., June, 2024


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