Anti-Gravity Walking by a Flying Humanoid Robot via Thrust-Rate Input Whole-Body Model Predictive Control

• URL: http://arxiv.org/abs/2609.07544v1
• Date: Mon, 07 Sep 2026 14:27:33 GMT
• Title: Anti-Gravity Walking by a Flying Humanoid Robot via Thrust-Rate Input Whole-Body Model Predictive Control
• Abstract summary: We propose and implement a real-time whole-body MPC framework for anti-gravity walking.<n>First, we formulate whole-body MPC using the time derivative of thrust, namely thrust-rate, as the control input.<n>Second, we address the lack of natural support forces in anti-gravity environments.
• Score: 10.590519863878098
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Flying humanoids are expected to perform tasks in diverse environments, while their existing locomotion is mainly limited to aerial flight and ground walking. The capability to move in complex three-dimensional space can greatly expand their application range. For such walking motion on ceilings and similar anti-gravity environments, whole-body MPC is effective. However, the discontinuous changes in dynamic structure accompanying contact switching during walking can induce thrust spikes, resulting in control instability. Therefore, in this work, we propose and implement a real-time whole-body MPC framework for anti-gravity bipedal walking. First, we formulate whole-body MPC using the time derivative of thrust, namely thrust-rate, as the control input. This formulation guarantees continuity of the thrust trajectory during contact switching while preserving the sparse structure of the optimal control problem for fast computation. Second, we address the lack of natural support forces in anti-gravity environments. We introduce lower bounds on the foot-normal component of the contact force, and smoothly transfer them during the doublesupport phase. Finally, we implement the proposed framework and demonstrate anti-gravity walking by a flying humanoid through simulation and a hardware experiment. To the best of our knowledge, this is the first demonstration of multi-contact whole-body MPC for a transformable aerial robot and walking by a flying humanoid beyond the ground.

Related papers

• Perceptive Humanoid Parkour: Chaining Dynamic Human Skills via Motion Matching [77.28042137892943]
  We present Perceptive Humanoid Parkour (PHP), a modular framework that enables humanoid robots to autonomously perform long-horizon, vision-based parkour.<n>We train motion-tracking reinforcement learning expert policies for these composed motions, and distill them into a single depth-based, multi-skill student policy.<n>We validate our framework with extensive real-world experiments on a Unitree G1 humanoid robot.
  arXiv  Detail & Related papers  (2026-02-17T18:59:11Z)
• HUSKY: Humanoid Skateboarding System via Physics-Aware Whole-Body Control [67.58927286226925]
  We propose a learning-based framework that integrates humanoid-skateboard system modeling and physics-aware whole-body control.<n>Experiments on the Unitree G1 humanoid platform demonstrate that our framework enables stable and agile maneuvering on skateboards in real-world scenarios.
  arXiv  Detail & Related papers  (2026-02-03T07:18:01Z)
• TriphiBot: A Triphibious Robot Combining FOC-based Propulsion with Eccentric Design [9.930529399697557]
  Triphibious robots capable of multi-domain motion are promising to handle complex tasks across diverse environments.<n>Existing designs primarily focus on dual-mode platforms, and some designs suffer from high mechanical complexity or low propulsion efficiency.<n>We propose a novel triphibious robot capable of aerial, terrestrial, and aquatic motion, by a minimalist design combining a quadcopter structure with two passive wheels.
  arXiv  Detail & Related papers  (2026-02-01T18:38:22Z)
• Deep Whole-body Parkour [33.232856360240106]
  We present a framework where exteroceptive sensing is integrated into whole-body motion tracking.<n>We demonstrate the non-trivial benefit of integrating perception into the control loop.<n>Results show that this framework enables robust, highly dynamic multi-contact motions, such as vaulting and dive-rolling, on unstructured terrain.
  arXiv  Detail & Related papers  (2026-01-12T16:33:16Z)
• Locomotion Beyond Feet [35.148387990120014]
  Locomotion Beyond Feet is a comprehensive system for whole-body humanoid locomotion across challenging terrains.<n>Our approach addresses two key challenges: contact-rich motion planning and generalization across diverse terrains.<n>Keyframes encode human knowledge of motor skills, are embodiment-specific, and can be readily validated in simulation or on hardware.
  arXiv  Detail & Related papers  (2026-01-07T05:36:39Z)
• Gait-Adaptive Perceptive Humanoid Locomotion with Real-Time Under-Base Terrain Reconstruction [86.53248703859718]
  We introduce a perceptive locomotion framework that merges terrain sensing, gait regulation, and whole-body control into a single reinforcement learning policy.<n>Experiments conducted on a 31-DoF, 1.65 m humanoid robot demonstrate robust locomotion in both simulation and real-world settings.
  arXiv  Detail & Related papers  (2025-12-08T11:42:41Z)
• Humanoid Whole-Body Locomotion on Narrow Terrain via Dynamic Balance and Reinforcement Learning [54.26816599309778]
  We propose a novel whole-body locomotion algorithm based on dynamic balance and Reinforcement Learning (RL)<n> Specifically, we introduce a dynamic balance mechanism by leveraging an extended measure of Zero-Moment Point (ZMP)-driven rewards and task-driven rewards in a whole-body actor-critic framework.<n> Experiments conducted on a full-sized Unitree H1-2 robot verify the ability of our method to maintain balance on extremely narrow terrains.
  arXiv  Detail & Related papers  (2025-02-24T14:53:45Z)
• Learning Humanoid Standing-up Control across Diverse Postures [27.79222176982376]
  Standing-up control is crucial for humanoid robots, with the potential for integration into current locomotion and loco-manipulation systems.<n>We present HoST (Humanoid Standing-up Control), a reinforcement learning framework that learns standing-up control from scratch.<n>Our experimental results demonstrate that the controllers achieve smooth, stable, and robust standing-up motions across a wide range of laboratory and outdoor environments.
  arXiv  Detail & Related papers  (2025-02-12T13:10:09Z)
• A Unified and General Humanoid Whole-Body Controller for Versatile Locomotion [30.418274871034775]
  We propose HugWBC: a unified and general humanoid whole-body controller for versatile locomotion.<n>By designing a general command space in the aspect of tasks and behaviors, HugWBC enables real-world humanoid robots to produce various natural gaits.<n>HugWBC also supports real-time interventions from external upper-body controllers like teleoperation, enabling loco-manipulation with precision.
  arXiv  Detail & Related papers  (2025-02-05T14:26:01Z)
• Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control [106.32794844077534]
  This paper presents a study on using deep reinforcement learning to create dynamic locomotion controllers for bipedal robots. We develop a general control solution that can be used for a range of dynamic bipedal skills, from periodic walking and running to aperiodic jumping and standing. This work pushes the limits of agility for bipedal robots through extensive real-world experiments.
  arXiv  Detail & Related papers  (2024-01-30T10:48:43Z)
• Agile Maneuvers in Legged Robots: a Predictive Control Approach [20.55884151818753]
  We present a contact-phase predictive and state-feedback controllers that enables legged robots to plan and perform agile locomotion skills. Our work is the first to show that predictive control can handle actuation limits, generate agile locomotion maneuvers and execute locally optimal feedback policies on hardware without the use of a separate whole-body controller.
  arXiv  Detail & Related papers  (2022-03-14T23:32:17Z)
• Learning Quadrupedal Locomotion over Challenging Terrain [68.51539602703662]
  Legged locomotion can dramatically expand the operational domains of robotics. Conventional controllers for legged locomotion are based on elaborate state machines that explicitly trigger the execution of motion primitives and reflexes. Here we present a radically robust controller for legged locomotion in challenging natural environments.
  arXiv  Detail & Related papers  (2020-10-21T19:11:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.