Hierarchical Robot Control

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17 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Hierarchical Reinforcement Learning for Collision-Free Locomotion of an Underactuated Biped
Oct 5, 2026Jagannath Prasad Sahoo, Saurabh Kumar, Surya Prakash S. K. +3Legged Robotics Hierarchical RL
CAIR, Indian Institute of Technology Mandi, Mandi, India · SMME, Indian Institute of Technology Mandi, Mandi, India
WayFinder: Hierarchical Visual-Language-Action for Zero-Shot Waypoint Generation and Low-Level Kinematic Control
Sep 29, 2026Timothy K Johnsen, Marco LevoratoRobotic Control Large Language Model-Based Robot Planning
San Diego State University and The University of California, Irvine in CA, USA · University of California, Irvine in CA, USA
GloVLA: Let Geometry Move and Local VLA Interact for Robust Object-Centric Manipulation in Unstructured Environments
Sep 5, 2026Truong Thanh Nguyen, Huy Hoang Nguyen, Ha Anh Nguyen +5Hierarchical Robot Control Robotic Manipulation
VinRobotics, Vietnam · Austrian Institute of Technology, Vienna, Austria · Hanoi University of Science and Technology, Vietnam +5
ABot-N1: Toward a General Visual Language Navigation Foundation Model
Jul 11, 2026Ruiyan Gong, Yingnan Guo, Junjun Hu +43Robotic Control Robot Foundation Models
High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning
Jul 3, 2026Yanzhou Li, Guangli Chen, Xiao-Meng Li +3Multi-Robot Formation Control Multi-Robot Systems
Dongguan Key Laboratory of Intelligent Equipment and Smart Industry, School of Advanced Engineering, Great Bay University, Dongguan 523000, China · Chair of Applied Statistics, Technische Universität Dresden, Dresden, Germany · School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China +2
3D HAMSTER: Bridging Planning and Control in Hierarchical Vision Language Action Models through 3D Trajectory Guidance
Jun 30, 2026Dongyoon Hwang, Byungkun Lee, Dongjin Kim +7Robotic Control Vision-Language-Action Models
Kim Jaechul Graduate School of AI, KAIST, Seoul, Republic of Korea · Graduate School of Artificial Intelligence, POSTECH, Pohang, Republic of Korea · Holiday Robotics, Seoul, Republic of Korea +1
EmbodiedUS-FS: Fast Slow Intelligence for Ultrasound Robotics
Jun 21, 2026Fangzhuo Zhang, Xinyu Wang, Xiao Yang +1Robotic Control Robotics
independent researcher · University of Wyoming · Shanghai World Foreign Language Academy +1
Mobile Pedipulation for Object Sliding via Hierarchical Control on a Wheeled Bipedal Robot
Jun 17, 2026Yue Qin, Yulun Zhuang, Zelin Shen +1Nonlinear MPC Robotic Manipulation
Department of Robotics, University of Michigan, Ann Arbor, MI 48109, USA
MotionPyramid: Hierarchical Motion Representation and Residual Interfaces
Jun 15, 2026Gao Zhu, Zaishuo Xia, Yubei ChenLatent Action Learning Hierarchical Representation Learning
UC Davis
Hierarchical Policies from Verbal and Egocentric Human Signals for Natural Human-Robot Interaction
Jun 9, 2026Dongjun Lee, Juheon Choi, Dong Kyu Shin +2Robotic Control Language-Conditioned Robot Control
1KAIST · 2Seoul National University · 3Config
GHOST: Hierarchical Sub-Goal Policies for Generalizing Robot Manipulation
Jun 8, 2026Sriram Krishna, Ben Eisner, Haotian Zhan +5Robot Policy Learning Robot Skill Learning
Robotics Institute, Carnegie Mellon University · UMass Amherst
HiVLA: A Visual-Grounded-Centric Hierarchical Embodied Manipulation System
Apr 15, 2026Tianshuo Yang, Guanyu Chen, Yutian Chen +8Hierarchical Robot Control Visual Grounding for Robotics
The University of Hong Kong · Shanghai AI Laboratory · Shanghai Jiao Tong University +1
Veo-Act: Enhancing VLA Policies with Frontier Video Models
Apr 6, 2026Zhongru Zhang, Chenghan Yang, Qingzhou Lu +4Robotic Control Language-Conditioned Robot Manipulation
Tsinghua University
Hierarchical Planning with Latent World Models
Apr 3, 2026Wancong Zhang, Basile Terver, Artem Zholus +7Hierarchical Planning Hierarchical Robot Control
FAIR at Meta · New York University · Mila - Québec AI Institute +1
Hierarchical Task Model Predictive Control for Sequential Mobile Manipulation Tasks
Mar 10, 2026Xintong Du, Siqi Zhou, Angela P. SchoelligHierarchical Robot Control Mobile Manipulation
Learning Systems and Robotics Lab (www.learnsyslab.org) at the Technical University of Munich, Germany, and the University of Toronto Institute for Aerospace Studies, Canada · University of Toronto Robotics Institute · Munich Institute of Robotics and Machine Intelligence (MIRMI) +1
Bi-HIL: Bilateral Control-Based Multimodal Hierarchical Imitation Learning via Subtask-Level Progress Rate and Keyframe Memory for Long-Horizon Contact-Rich Robotic Manipulation
Mar 4, 2026Thanpimon Buamanee, Masato Kobayashi, Yuki UranishiLong-Horizon Robotic Manipulation Robot Imitation Learning
The University of Osaka · Kobe University
HierKick: Hierarchical Reinforcement Learning for Vision-Guided Soccer Robot Control
Mar 1, 2026Yizhi Chen, Zheng Zhang, Zhanxiang Cao +7Robotic Control Robotic RL
Tongji University, Shanghai, China. · Shanghai Innovation Institute, Shanghai, China. · Shanghai Jiao Tong University, Shanghai, China. +2

Oct 5, 2026J/K move · Enter open · S save

Hierarchical Reinforcement Learning for Collision-Free Locomotion of an Underactuated Biped

Jagannath Prasad Sahoo, Saurabh Kumar, Surya Prakash S. K., Samiran Datta+2

CAIR, Indian Institute of Technology Mandi, Mandi, India · SMME, Indian Institute of Technology Mandi, Mandi, India

Legged Robotics Hierarchical RL Robot Locomotion RL for Legged Locomotion Hierarchical Robot Control

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A bipedal robot cannot deviate from its path to avoid an obstacle without disturbing its balance, and this coupling is most severe on underactuated platforms such as the biped considered here, which has four actuated joints per leg and no hip or ankle roll. This paper presents a Hierarchical Reinforcement Learning (HRL) framework in which a High-Level (HL) policy observes the robot pose, 36 raycast proximity measurements, moving-obstacle states, and a receding-horizon local goal, and outputs a body-velocity command

(v_x, v_y, ω_{yaw})

every ten control steps, while a velocity-conditioned Low-Level (LL) policy tracks each command through PD-controlled joint targets. Both policies are trained jointly with Soft Actor-Critic (SAC). Because the converged gait is task-agnostic, it is frozen and driven by classical planners over the same command interface, yielding three controlled baselines: SAC+A*, SAC+RRT*, and SAC+APF. Across 100 evaluation trials per method in randomized PyBullet environments, the proposed method reaches the goal in 98.0% of static and 88.0% of dynamic trials, against at most 78.0% and 68.0% for the planner hybrids, with path lengths within 4% of the A* reference, and ablations confirm that each observation channel and reward term contributes materially to this performance.