More Like this

1. Momentum based Whole-Body Optimal Planning for a Single-Spherical-Wheeled Balancing Mobile Manipulator

   Shu, R.; Hollis, R. (October 2021, IEEE International Conference on Robotics and Automation)

   null (Ed.)

   In this paper, we present a planning and control framework for dynamic, whole-body motions for dynamically stable shape-accelerating mobile manipulators. This class of robots are inherently unstable and require careful coordination between the upper and lower body to maintain balance while performing manipulation tasks. Solutions to this problem either use a complex, full-body nonlinear dynamic model of the robot or a highly simplified model of the robot. Here we explore the use of centroidal dynamics which has recently become a popular approach for designing balancing controllers for humanoid robots. We describe a framework where we first solve a trajectory optimization problem offline. We define balancing for a ballbot in terms of the centroidal momentum instead of other approaches like ZMP or angular velocity that are more commonly used. The generated motion is tracked using a PD- PID cascading balancing controller for the body and torque controller for the arms. We demonstrate that this framework is capable of generating dynamic motion plans and control inputs with examples on the CMU ballbot, a single-spherical-wheeled balancing mobile manipulator. 

   more » « less
2. Dynamic locomotion for passive-ankle biped robots and humanoids using whole-body locomotion control

   https://doi.org/10.1177/0278364920918014  

   Kim, Donghyun; Jorgensen, Steven_Jens; Lee, Jaemin; Ahn, Junhyeok; Luo, Jianwen; Sentis, Luis (June 2020, The International Journal of Robotics Research)

   Whole-body control (WBC) is a generic task-oriented control method for feedback control of loco-manipulation behaviors in humanoid robots. The combination of WBC and model-based walking controllers has been widely utilized in various humanoid robots. However, to date, the WBC method has not been employed for unsupported passive-ankle dynamic locomotion. As such, in this article, we devise a new WBC, dubbed the whole-body locomotion controller (WBLC), that can achieve experimental dynamic walking on unsupported passive-ankle biped robots. A key aspect of WBLC is the relaxation of contact constraints such that the control commands produce reduced jerk when switching foot contacts. To achieve robust dynamic locomotion, we conduct an in-depth analysis of uncertainty for our dynamic walking algorithm called the time-to-velocity-reversal (TVR) planner. The uncertainty study is fundamental as it allows us to improve the control algorithms and mechanical structure of our robot to fulfill the tolerated uncertainty. In addition, we conduct extensive experimentation for: (1) unsupported dynamic balancing (i.e., in-place stepping) with a six-degree-of-freedom biped, Mercury; (2) unsupported directional walking with Mercury; (3) walking over an irregular and slippery terrain with Mercury; and 4) in-place walking with our newly designed ten-DoF viscoelastic liquid-cooled biped, DRACO. Overall, the main contributions of this work are on: (a) achieving various modalities of unsupported dynamic locomotion of passive-ankle bipeds using a WBLC controller and a TVR planner; (b) conducting an uncertainty analysis to improve the mechanical structure and the controllers of Mercury; and (c) devising a whole-body control strategy that reduces movement jerk during walking. 

   more » « less
3. Hierarchical World Models as Visual Whole-Body Humanoid Controllers

   Hansen, Nicklas; V, Jyothir S; Sobal, Vlad; LeCun, Yann; Wang, Xiaolong; Su, Hao (April 2025, The International Conference on Learning Representations (ICLR 2025))

   Whole-body control for humanoids is challenging due to the high-dimensional nature of the problem, coupled with the inherent instability of a bipedal morphology. Learning from visual observations further exacerbates this difficulty. In this work, we explore highly data-driven approaches to visual whole-body humanoid control based on reinforcement learning, without any simplifying assumptions, reward design, or skill primitives. Specifically, we propose a hierarchical world model in which a high-level agent generates commands based on visual observations for a low-level agent to execute, both of which are trained with rewards. Our approach produces highly performant control policies in 8 tasks with a simulated 56-DoF humanoid, while synthesizing motions that are broadly preferred by humans. 

   more » « less
4. MIRA: Multi-Joint Imitation with Recurrent Adaptation for Robot-Assisted Rehabilitation

   https://doi.org/10.3390/technologies12080135  

   Ashary, Ali; Mishra, Ruchik; Rayguru, Madan M; Popa, Dan O (August 2024, Technologies)

   This work proposes a modular learning framework (MIRA) for rehabilitation robots based on a new deep recurrent neural network (RNN) that achieves adaptive multi-joint motion imitation. The RNN is fed with the fundamental frequencies as well as the ranges of the joint trajectories, in order to predict the future joint trajectories of the robot. The proposed framework also uses a Segment Online Dynamic Time Warping (SODTW) algorithm to quantify the closeness between the robot and patient motion. The SODTW cost decides the amount of modification needed in the inputs to our deep RNN network, which in turn adapts the robot movements. By keeping the prediction mechanism (RNN) and adaptation mechanism (SODTW) separate, the framework achieves modularity, flexibility, and scalability. We tried both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) RNN architectures within our proposed framework. Experiments involved a group of 15 human subjects performing a range of motion tasks in conjunction with our social robot, Zeno. Comparative analysis of the results demonstrated the superior performance of the LSTM RNN across multiple task variations, highlighting its enhanced capability for adaptive motion imitation. 

   more » « less
5. Time-Varying Foot Placement Control for Humanoid Walking on Swaying Rigid Surface

   Gao, Yuan; Paredes, Victor; Gong, Yukai Gong; He, Zijian; Hereid, Ayonga; Gu, Yan (August 2025, IEEE transactions on robotics)

   Locomotion on dynamic rigid surface (i.e., rigid surface accelerating in an inertial frame) presents complex challenges for controller design, which are essential to address for deploying humanoid robots in dynamic real-world environments such as moving trains, ships, and airplanes. This paper introduces a real-time, provably stabilizing control approach for humanoid walking on periodically swaying rigid surface. The first key contribution is an analytical extension of the classical angular momentum-based linear inverted pendulum model from static to swaying grounds whose motion period may be different than the robot’s gait period. This extension results in a time-varying, nonhomogeneous robot model, which is fundamentally different from the existing pendulum models. We synthesize a discrete footstep control law for the model and derive a new set of sufficient stability conditions that verify the controller’s stabilizing effect. Finally, experiments conducted on a Digit humanoid robot, both in simulations and on hardware, demonstrate the framework’s effectiveness in addressing bipedal locomotion on swaying ground, even under uncertain surface motions and unknown external pushes. 

   more » « less