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Zhongyu Li

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Preprint Sep 2026

Force-Aware Reinforcement Learning with Hybrid Sensorless Force Estimation for Wheeled-Legged Loco-Manipulation

Force-controlled loco-manipulation requires a whole-body policy to coordinate locomotion and arm motion while regulating end-effector interaction forces. This is challenging under floating-base dynamics and changing support contacts, particularly when end-effector force/torque sensing is unavailable for control. This p...

Xuanqi Zeng, Jia-Ming Wang, Tian-Lin Zhang et al. · 0 citations
Preprint Sep 2026

A Tendon-Driven Robotic Jellyfish with Constrained Soft Actuation and Depth Control via Reinforcement Learning

Jellyfish-inspired robots offer a compliant and efficient approach to underwater locomotion, but achieving large deformation together with repeatable actuation and closed-loop control remains challenging. In this work, we present a tendon-driven robotic jellyfish with constrained soft actuation. Each actuator combines...

Jia-Rui Peng, Yu-Tong Wu, Ze-Long Wang et al. · 0 citations
Preprint Sep 2026

A Support-Enhanced Granular-Jamming Gripper for RL-based Grasping with Continuum Manipulators

Continuum manipulators provide dexterous motion in confined spaces, but structural compliance, hysteresis, and load-dependent deformation leave residual position and orientation errors that can undermine reliable contact with rigid grippers. To address this limitation, this paper presents a lightweight support-enhanced...

Dan-Yu Liu, Tian-Lin Zhang, Wei Chen et al. · 0 citations
Preprint Sep 2026

HiRE: Hindsight Reward Editing for Policy Finetuning

Hindsight Reward Editing (HiRE) bridges the broad knowledge of foundation representation models with physical control awareness, by contrasting successful and failed trajectories in hindsight, and consistently outperforms other reward recipes by delivering dense, control-aware feedback that prevents value function coll...

Haoyi Niu, Zhen Han, Yu-Feng Ji et al. · 0 citations
Preprint Sep 2026

Humanoid Badminton: Learning Dynamic Racket Skills from Limited Human Motion Data

This is the first real-world humanoid racket-sport system to demonstrate multi-skill human--robot rallies including highly dynamic jump returns, and is the first real-world humanoid racket-sport system to demonstrate multi-skill human--robot rallies including highly dynamic jump returns.

Jing-Zhi Cui, Zhe-Xiong Wang, Bang-Jie Xu et al. · 0 citations
Preprint Jul 2026

Action QFormer: Structured Representation Shaping under Action Supervision in Vision-Language-Action Models

Action supervision in vision-language-action (VLA) models is often treated as a downstream objective for learning action prediction. In this paper, we study it instead as a force that shapes inherited multimodal representations. We show that this shaping has a dual effect: it is necessary for forming action-compatible...

Yufeng Ji, Wenhao Tang, Haoyi Niu et al. · 0 citations

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