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Tengyu Liu

5 papers hereh-index 354 citations5 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author5

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.RO3
  • cs.CV1
  • eess.SY1
same name
  • Tengyu Liu — 15 papers, h 21
  • Tengyu Liu — 1 paper, h 6
  • Tengyu Liu — 1 paper
  • Tengyu Liu — 1 paper, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

collaborators

5 papers

eess.SY2026

On Feedback Speed Control for a Planar Tracking

Xincheng Li, Tengyue Liu, Udit Halder

This paper investigates a planar tracking problem between a leader and follower agent. We propose a novel feedback speed control law, paired with a constant bearing steering strate…

cs.RO2026

OmniTrack: General Motion Tracking via Physics-Consistent Reference

Yuhan Li, Peiyuan Zhi, Yunshen Wang +6

Learning motion tracking from rich human motion data is a foundational task for achieving general control in humanoid robots, enabling them to perform diverse behaviors. However, d…

cs.RO2025

Dexterous Manipulation through Imitation Learning: A Survey

Shan An, Ziyu Meng, Chao Tang +9

Dexterous manipulation, which refers to the ability of a robotic hand or multi-fingered end-effector to skillfully control, reorient, and manipulate objects through precise, coordi…

cs.RO2025

SafeFall: Learning Protective Control for Humanoid Robots

Ziyu Meng, Tengyu Liu, Le Ma +4

Bipedal locomotion makes humanoid robots inherently prone to falls, causing catastrophic damage to the expensive sensors, actuators, and structural components of full-scale robots.…

cs.CV2025

GROVE: A Generalized Reward for Learning Open-Vocabulary Physical Skill

Jieming Cui, Tengyu Liu, Ziyu Meng +5

Learning open-vocabulary physical skills for simulated agents presents a significant challenge in artificial intelligence. Current reinforcement learning approaches face critical l…

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