Humanoid Robot Control

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26 papers in the last four weeks, up 767% on the four weeks before. 0.3% of all new papers.

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FADA: Few-Shot Domain Adaptation via Dynamics Alignment for Humanoid Control
Jun 26, 2026Angchen Xie, Nikhil Sobanbabu, Ishayu Shikhare +3Few-Shot Domain Adaptation Humanoid Robot Control
Carnegie Mellon University
CWI: Composite Humanoid Whole-Body Imitation System for Loco-manipulation
Jun 26, 2026Wenqi Ge, Junde Guo, Zhen Fu +3Humanoid Loco-Manipulation Humanoid Robot Control
LimX Dynamics, Shenzhen 518055, China. · The University of Hong Kong, Hong Kong SAR, China. · School of Automation and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen 518055, China. +2
SceneBot: Contact-Prompted General Humanoid Whole Body Tracking with Scene-Interaction
Jun 25, 2026Sirui Chen, Shibo Zhao, Zhen Wu +3Robotic Control Humanoid Robot Locomotion
Stanford, Amazon FAR United States · Amazon FAR United States · CMU, Amazon FAR United States
PressMimic: Pressure-Guided Motion Capture and Control for Humanoid Robot Imitation
Jun 25, 2026Yi Lu, Shenghao Ren, Tianyu Xiong +6Markerless Motion Capture Humanoid Robot Control
School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China. · BNRist, Tsinghua University, Beijing 100084, China. · Key Laboratory of Optoelectronic Devices and Systems with Extreme Performances of MOE, Nanjing University, Nanjing 210023, China.
A System for Fast, Resilient, and Adaptable Loco-Manipulation Behaviors on Humanoid Robots
Jun 24, 2026Duncan William CalvertHumanoid Loco-Manipulation Humanoid Robot Control
Department of Intelligent Systems and Robotics, THE UNIVERSITY OF WEST FLORIDA, HAL MARCUS COLLEGE OF SCIENCE AND ENGINEERING, US
TaskNPoint: How to Teach Your Humanoid to Hit a Backhand in Minutes
Jun 24, 2026Blake Werner, Ilona Demler, Pietro Perona +1RL for Robotics Robot Skill Learning
California Institute of Technology
Learning Asynchronous Upper-body Task-space Trajectory Tracking Policy for Humanoid Robots
Jun 24, 2026Yumeng Liu, Dongqi Wang, Jiyu Yu +3Robot Trajectory Tracking Robot Policy Learning
Zhejiang University
RGB: RL Guided Whole-Body MPPI for Humanoid Control
Jun 23, 2026Yunsoo Seo, Sol Choi, Euncheol Im +2Robot Trajectory Tracking Humanoid Robot Control
Center for Humanoid Research, Korea Institute of Science and Technology (KIST), 02792 Seoul, South Korea · University of Texas at Austin, Austin, Texas 78712, USA · Department of Mechanical Engineering, Yonsei University, 03722, Seoul, South Korea +1
BFMTrack: Latent Sequence Optimization for Physics-Based Motion Tracking with Behavioral Foundation Models
Jun 23, 2026Thomas Rupf, Agon Serifi, David Müller +4Humanoid Robot Control
Disney Research, Zurich, Switzerland
TEXEDO : Test Time Scaling for Controller-aware Language-conditioned Humanoid Motion Generation
Jun 22, 2026Jianuo Cao, Yuxin Chen, Yuzhen Song +3Humanoid Robot Control Test-Time Scaling
Nanjing University · University of California, Berkeley · Southern University of Science and Technology
HALOMI: Learning Humanoid Loco-Manipulation with Active Perception from Human Demonstrations
Jun 17, 2026Zehui Zhao, Yuxuan Zhao, Gaojing Zhang +3Active Perception Humanoid Loco-Manipulation
Shanghai Jiao Tong University · University of Sussex · East China University of Science and Technology
HumanoidArena: Benchmarking Egocentric Hierarchical Whole-body Learning
Jun 16, 2026Taowen Wang, Zikang Xie, Bin Yang +13Humanoid Robot Control Hierarchical RL
The Hong Kong University of Science and Technology (Guangzhou) · Beijing University of Technology · Harbin Institute of Technology, Shenzhen +2
MotionPyramid: Hierarchical Motion Representation and Residual Interfaces
Jun 15, 2026Gao Zhu, Zaishuo Xia, Yubei ChenLatent Action Learning Hierarchical Representation Learning
UC Davis
VENOM: Versatile Embodied Network for Omni-bodied Motion tracking
Jun 15, 2026Siddharth Padmanabhan, Kazuki Miyazawa, Takato HoriiRobotic Control Humanoid Robot Control
Graduate School of Engineering Science, University of Osaka, 1-3 Machikaneyama-cho, Toyonaka-shi, Osaka 560-8531, Japan
Energy-Efficient Arm Reaching for a Humanoid Robot via Deep Reinforcement Learning with Identified Power Models
Jun 14, 2026Nestor N. Deniz, Simon Parsons, Fernando Auat CheeinRobotic Control Humanoid Robot Control
Engineering Department at Harper Adams University, Newport, Shropshire TF10 8NB, UK · Lincoln Institute for Agri-Food Technology and Lincoln Centre for Autonomous Systems
Critic Architecture Matters: Dual vs. Unified Critics for Humanoid Loco-Manipulation
Jun 10, 2026Mehmet Turan YardımcıMulti-Objective Reinforcement Learning Humanoid Loco-Manipulation
Department of Computer Engineering C¸ukurova University Adana, Türkiye
RoboNaldo: Accurate, Stable and Powerful Humanoid Soccer Shooting via Motion-Guided Curriculum Reinforcement Learning
Jun 9, 2026Yichao Zhong, Yidan Lu, Yuhang Lu +9Reinforcement Learning Robot Skill Learning
The University of Hong Kong · Archon Robotics · The Chinese University of Hong Kong
A Spiking Neural Architecture for Coordinating Arm and Locomotor Control
Jun 9, 2026Lea Steffen, Kathryn Simone, Graeme Damberger +3Humanoid Robot Locomotion Humanoid Robot Control
Centre for Theoretical Neuroscience Dept. of Systems Design Engineering University of Waterloo Waterloo, Canada · Applied Brain Research Waterloo, Canada · Dept. of Nanotechnology Engineering University of Waterloo Waterloo, Canada +1
OMG: Omni-Modal Motion Generation for Generalist Humanoid Control
Jun 9, 2026Siqiao Huang, Kun-Ying Lee, Dongming Qiao +5Robot Foundation Models Humanoid Robot Control
Tsinghua University
VAIC: Vision-Guided Humanoid Agile Object Interaction Control via Decoupled Commands
Jun 8, 2026Dongting Li, Qianyang Wu, Xingyu Chen +9Humanoid Robot Control Robotic Manipulation
Tsinghua University · Xiaomi Robotics Lab · HKUST(Guangzhou)
MotionWAM: Towards Foundation World Action Models for Real-Time Humanoid Loco-Manipulation
Jun 8, 2026Jia Zheng, Teli Ma, Yudong Fan +3Humanoid Loco-Manipulation Humanoid Robot Control
Mondo Robotics 2 HKUST (GZ) · Mondo Robotics · HKUST (GZ) 3 HKUST
EgoPriMo: Egocentric Motion Generation for Interactive Humanoid Control
Jun 7, 2026Haoyang Ge, Peng Ren, Yukun Shi +3Humanoid Robot Control Human Motion Generation
Tianjin University, Tianjin, China · Zhongguancun Academy, Beijing, China · Beihang University, Beijing, China +2
Perceptive Behavior Foundation Model: Adapting Human Motion Priors to Robot-Centric Terrain
Jun 6, 2026Zifan Wang, Yizhao Li, Teli Ma +5Humanoid Robot Locomotion Robot Foundation Models
1Mondo Robotics · 2The Hong Kong University of Science and Technology (Guangzhou) · 4Artificial General Intelligence Institute, University of Science and Technology of China +2
HANDOFF: Humanoid Agentic Task-Space Whole-Body Control via Distilled Complementary Teachers
Jun 4, 2026Lizhi Yang, Junheng Li, Nehar Poddar +5Humanoid Loco-Manipulation Robot Skill Learning
California Institute of Technology · The Institute for Human & Machine Cognition
M3imic: Learning a Versatile Whole-Body Controller for Multimodal Motion Mimicking
Jun 3, 2026Zuxing Lu, Ziang Zheng, Yao Lyu +7RL for Robotics Humanoid Robot Control
School of Automation, Southeast University, Nanjing 210096, China · School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China · Department of Robotics, Mohamed Bin Zayed University of Artificial Intelligence, Abu Dhabi 7909, UAE
Humanoid-GPT: Scaling Data and Structure for Zero-Shot Motion Tracking
Jun 2, 2026Zekun Qi, Xuchuan Chen, Dairu Liu +10Zero-Shot Learning Transformer
Tsinghua University · Galbot Inc. · Shanghai Jiao Tong University +2
Bionic Human-Motion Style Transfer for Physically Executable Whole-Body Control of Humanoid Robots
Jun 2, 2026Tianchen Huang, Mingkuan Zhao, Yang Gao +8Humanoid Robot Control Human Motion Generation
Institute of Humanoid Robots, Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui, 230026, China. · School of Computer Science and Technology, Faculty of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, 710049, China. · School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, 730000, China.
Constrained Whole-Body Tracking for Humanoid Robots
May 29, 2026Daniel Morton, Pranit Mohnot, Marco PavoneRobotic Control Robot Trajectory Tracking
Stanford University · NVIDIA Research
HOIST: Humanoid Optimization with Imitation and Sample-efficient Tuning for Manipulating Suspended Loads
May 29, 2026Songyang Liu, Shunyu Yao, Dingyuan Huang +1RL for Robotics Humanoid Robot Control
Department of Civil and Coastal Engineering University of Florida United States
Towards Shared Embodied Intelligence in Humanoid Robots through Optimization Development and Testing of the Human Aware ergoCub Robot
May 26, 2026Carlotta Sartore, Mohamed Elobaid, Lorenzo Rapetti +13Humanoid Robot Control Human-Robot Collaboration
GenerativeBionics, Genova, Italy. · 2Artificial and Mechanical Intelligence, Istituto Italiano di Tecnologia, Genova, Italy. · School of Computer Science, University of Manchester, Manchester, United Kingdom. +3

Jun 26, 2026, cs.ROJ/K move · Enter open · S save

FADA: Few-Shot Domain Adaptation via Dynamics Alignment for Humanoid Control

Angchen Xie, Nikhil Sobanbabu, Ishayu Shikhare, Alan Wang+2

Carnegie Mellon University

Few-Shot Domain Adaptation Humanoid Robot Control Few-Shot Learning

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High-precision humanoid control is limited by target-domain dynamics mismatch, where the same control objective can induce different realized motions under changes in terrain, payload, or actuator response. Existing methods either pursue zero-shot transfer through domain randomization or in-context adaptation without target-domain specialization, or require heavy adaptation pipelines that leverage target-domain data, such as model calibration, residual learning, or policy retraining. In this paper, we present FADA (Few-Shot Domain Adaptation via Dynamics Alignment), a three-stage Planner-Inverse Dynamics Model (Planner-IDM) framework for few-shot adaptation in humanoid control. FADA first trains an oracle policy with privileged information and then distills the oracle behavior into a deployable Planner-IDM student through DAgger. At deployment, FADA freezes the planner and finetunes only the IDM using approximately 2 minutes of target-domain rollouts with standard supervised learning. Rather than requiring optimal demonstrations or rewards, FADA uses the paired actions and observations that are observed during these rollouts as supervision, aligning the IDM's action generation with target-domain dynamics. Experiments show that FADA outperforms both in-context and end-to-end adaptation baselines, improving task performance under dynamics shifts and enabling real humanoid robots to execute diverse high-precision whole-body tasks. Implementation details and qualitative hardware rollout videos are available at https://lecar-lab.github.io/FADA-humanoid/.

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