Author name cluster

Xiaobin Xiong

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16 papers

1 author row

IROS Conference 2025 Conference Paper

iWalker: Imperative Visual Planning for Walking Humanoid Robot

Xiao Lin
Yuhao Huang
Taimeng Fu
Xiaobin Xiong
Chen Wang

Humanoid robots, designed to operate in human-centric environments, serve as a fundamental platform for a broad range of tasks. Although humanoid robots have been extensively studied for decades, a majority of existing humanoid robots still heavily rely on complex modular frameworks, leading to inflexibility and potential compounded errors from independent sensing, planning, and acting components. In response, we propose an end-to-end humanoid sense-plan-act walking system, enabling vision-based obstacle avoidance and footstep planning for whole body balancing simultaneously. We designed two imperative learning (IL)-based bilevel optimizations for model-predictive step planning and whole body balancing, respectively, to achieve self-supervised learning for humanoid robot walking. This enables the robot to learn from arbitrary unlabeled data, improving its adaptability and generalization capabilities. We refer to our method as iWalker and demonstrate its effectiveness in both simulated and real-world environments, representing a significant advancement toward autonomous humanoid robots.

IROS Conference 2025 Conference Paper

Reference-Steering via Data-Driven Predictive Control for Hyper-Accurate Robotic Flying-Hopping Locomotion

Yicheng Zeng
Yuhao Huang
Xiaobin Xiong

State-of-the-art model-based control designs have been shown to be successful in realizing dynamic locomotion behaviors for robotic systems. The precision of the realized behaviors in terms of locomotion performance via fly, hopping, or walking has not yet been well investigated, despite the fact that the difference between the robot model and physical hardware is doomed to produce inaccurate trajectory tracking. To address this inaccuracy, we propose a referencing-steering method to bridge the model-to-real gap by establishing a data-driven input-output (DD-IO) model on top of the existing model-based design. The DD-IO model takes the reference tracking trajectories as the input and the realized tracking trajectory as the output. By utilizing data-driven predictive control, we steer the reference input trajectories online so that the realized output ones match the actual desired ones. We demonstrate our method on the robot PogoX to realize hyper-accurate hopping and flying behaviors in both simulation and hardware. This data-driven reference-steering approach is straightforward to apply to general robotic systems for performance improvement via hyper-accurate trajectory tracking.

ICRA Conference 2025 Conference Paper

Simultaneous Ground Reaction Force and State Estimation Via Constrained Moving Horizon Estimation

Jiarong Kang
Xiaobin Xiong

Accurate ground reaction force (GRF) estimation can significantly improve the adaptability of legged robots in various real-world applications. For instance, with estimated GRF and contact kinematics, the locomotion control and planning assist the robot in overcoming uncertain terrains. The canonical momentum-based methods, formulated as nonlinear observers, do not fully address the noisy measurements and the dependence between floating-base states and the generalized momentum dynamics. In this paper, we present a simultaneous ground reaction force and state estimation framework for legged robots, which systematically addresses the sensor noise and the coupling between states and dynamics. With the floating base orientation estimated separately, a decentralized Moving Horizon Estimation (MHE) method is implemented to fuse the robot dynamics, proprioceptive sensors, exteroceptive sensors, and deterministic contact complementarity constraints in a convex windowed optimization. The proposed method is shown to be capable of providing accurate GRF and state estimation on several legged robots, including the custom-designed humanoid robot Bucky, the open-source educational planar bipedal robot STRIDE, and the quadrupedal robot Unitree Go1, with a frequency of 200Hz and a past time window of 0. 04s.

IROS Conference 2024 Conference Paper

Data-Driven Predictive Control for Robust Exoskeleton Locomotion

Kejun Li
Jeeseop Kim
Xiaobin Xiong
Kaveh Akbari Hamed
Yisong Yue
Aaron D. Ames

Exoskeleton locomotion must be robust while being adaptive to different users with and without payloads. To address these challenges, this work introduces a data-driven predictive control (DDPC) framework to synthesize walking gaits for lower-body exoskeletons, employing Hankel matrices and a state transition matrix for its data-driven model. The proposed approach leverages DDPC through a multi-layer architecture. At the top layer, DDPC serves as a planner employing Hankel matrices and a state transition matrix to generate a data-driven model that can learn and adapt to varying users and payloads. At the lower layer, our method incorporates inverse kinematics and passivity-based control to map the planned trajectory from DDPC into the full-order states of the lower-body exoskeleton. We validate the effectiveness of this approach through numerical simulations and hardware experiments conducted on the Atalante lower-body exoskeleton with different payloads. Moreover, we conducted a comparative analysis against the model predictive control (MPC) framework based on the reduced-order linear inverted pendulum (LIP) model. Through this comparison, the paper demonstrates that DDPC enables robust bipedal walking at various velocities while accounting for model uncertainties and unknown perturbations.

IROS Conference 2024 Conference Paper

Explosive Legged Robotic Hopping: Energy Accumulation and Power Amplification via Pneumatic Augmentation

Yifei Chen
Arturo Gamboa-Gonzalez
Michael Wehner
Xiaobin Xiong

We present a novel pneumatic augmentation to traditional electric motor-actuated legged robot to increase intermittent power density to perform infrequent explosive hopping behaviors. The pneumatic system is composed of a pneumatic pump, a tank, and a pneumatic actuator. The tank is charged up by the pump during regular hopping motion that is created by the electric motors. At any time after reaching a desired air pressure in the tank, a solenoid valve is utilized to rapidly release the air pressure to the pneumatic actuator (piston) which is used in conjunction with the electric motors to perform explosive hopping, increasing maximum hopping height for one or subsequent cycles. We show that, on a custom-designed one-legged hopping robot, without any additional power source and with this novel pneumatic augmentation system, their associated system identification and optimal control, the robot is able to realize highly explosive hopping with power amplification per cycle by a factor of approximately 5. 4 times the power of electric motor actuation alone.

ICRA Conference 2024 Conference Paper

Terrestrial Locomotion of PogoX: From Hardware Design to Energy Shaping and Step-to-step Dynamics Based Control

Yi Wang 0099
Jiarong Kang
Zhiheng Chen
Xiaobin Xiong

We present a novel controller design on a robotic locomotor that combines an aerial vehicle with a spring-loaded leg. The main motivation is to enable the terrestrial locomotion capability on aerial vehicles so that they can carry heavy loads: heavy enough that flying is no longer possible, e. g. , when the thrust-to-weight ratio (TWR) is small. The robot is designed with a pogo-stick leg and a quadrotor, and thus it is named as PogoX. We show that with a simple and lightweight spring-loaded leg, the robot is capable of hopping with TWR <1. The control of hopping is realized via two components: a vertical height control via control Lyapunov function-based energy shaping, and a step-to-step (S2S) dynamics based horizontal velocity control that is inspired by the hopping of the Spring-Loaded Inverted Pendulum (SLIP). The controller is successfully realized on the physical robot, showing dynamic terrestrial locomotion of PogoX which can hop at variable heights and different horizontal velocities with robustness to ground height variations and external pushes.

IROS Conference 2023 Conference Paper

Data-Driven Adaptation for Robust Bipedal Locomotion with Step-to-Step Dynamics

Min Dai
Xiaobin Xiong
Jaemin Lee 0005
Aaron D. Ames

This paper presents an online framework for synthesizing agile locomotion for bipedal robots that adapts to unknown environments, modeling errors, and external disturbances. To this end, we leverage step-to-step (S2S) dynamics which has proven effective in realizing dynamic walking on underactuated robots-assuming known dynamics and environments. This paper considers the case of uncertain models and environments and presents a data-driven representation of the S2S dynamics that can be learned via an adaptive control approach that is both data-efficient and easy to implement. The learned S2S controller generates desired discrete foot placement, which is then realized on the full-order dynamics of the bipedal robot by tracking desired outputs synthesized from the given foot placement. The benefits of the proposed approach are twofold. First, it improves the ability of the robot to walk at a given desired velocity when compared to the non-adaptive baseline controller. Second, the data-driven approach enables stable and agile locomotion under the effect of various unknown disturbances: additional unmodeled payload, large robot model errors, external disturbance forces, biased velocity estimation, and sloped terrains. This is demonstrated through in-depth evaluation with a high-fidelity simulation of the bipedal robot Cassie subject to the aforementioned disturbances [1].

ICRA Conference 2022 Conference Paper

Bipedal Walking on Constrained Footholds: Momentum Regulation via Vertical COM Control

Min Dai
Xiaobin Xiong
Aaron D. Ames

This paper presents an online walking synthesis methodology to enable dynamic and stable walking on constrained footholds for underactuated bipedal robots. Our approach modulates the change of angular momentum about the foot-ground contact pivot at discrete impact using pre-impact vertical center of mass (COM) velocity. To this end, we utilize the underactuated Linear Inverted Pendulum (LIP) model for approximating the underactuated walking dynamics to provide the desired post-impact angular momentum for each step. Desired outputs are constructed via online optimization combined with closed-form polynomials and tracked via a quadratic program (QP) based controller. This method is demonstrated on two robots, AMBER and 3D Cassie, for which stable walking behaviors with constrained footholds are realized on flat ground, stairs, and randomly located stepping stones.

IROS Conference 2022 Conference Paper

From Human Walking to Bipedal Robot Locomotion: Reflex Inspired Compensation on Planned and Unplanned Downsteps

Joris Verhagen
Xiaobin Xiong
Aaron D. Ames
Ajay Seth

Humans are able to negotiate downstep behaviors-both planned and unplanned-with remarkable agility and ease. The goal of this paper is to systematically study the translation of this human behavior to bipedal walking robots, even if the morphology is inherently different. Concretely, we begin with human data wherein planned and unplanned downsteps are taken. We analyze this data from the perspective of reduced-order modelling of the human, encoding the center of mass (CoM) kinematics and contact forces, which allows for the translation of these behaviors into the corresponding reduced-order model of a bipedal robot. We embed the resulting behaviors into the full-order dynamics of a bipedal robot via nonlinear optimization-based controllers. The end result is the demonstration of planned and unplanned downsteps in simulation on an underactuated walking robot.

ICRA Conference 2021 Conference Paper

Global Position Control on Underactuated Bipedal Robots: Step-to-step Dynamics Approximation for Step Planning

Xiaobin Xiong
Jenna Reher
Aaron D. Ames

Global position control for underactuated bipedal walking is a challenging problem due to the lack of actuation on the feet of the robots. In this paper, we apply the Hybrid-Linear Inverted Pendulum (H-LIP) based stepping on 3D underactuated bipedal robots for global position control. The step-to-step (S2S) dynamics of the H-LIP walking approximates the actual S2S dynamics of the walking of the robot, where the step size is considered as the input. Thus the feedback controller based on the H-LIP approximately controls the robot to behave like the H-LIP, the differences between which stay in an error invariant set. Model Predictive Control (MPC) is applied to the H-LIP for global position control in 3D. The H-LIP stepping then generates desired step sizes for the robot to track. Moreover, turning behavior is integrated with the step planning. The proposed framework is verified on the 3D underactuated bipedal robot Cassie in simulation together with a proof-of-concept experiment.

IROS Conference 2020 Conference Paper

Energy-Efficient Motion Planning for Multi-Modal Hybrid Locomotion

H. J. Terry Suh
Xiaobin Xiong
Andrew Singletary
Aaron D. Ames
Joel W. Burdick

Hybrid locomotion, which combines multiple modalities of locomotion within a single robot, enables robots to carry out complex tasks in diverse environments. This paper presents a novel method for planning multi-modal locomotion trajectories using approximate dynamic programming. We formulate this problem as a shortest-path search through a state-space graph, where the edge cost is assigned as optimal transport cost along each segment. This cost is approximated from batches of offline trajectory optimizations, which allows the complex effects of vehicle under-actuation and dynamic constraints to be approximately captured in a tractable way. Our method is illustrated on a hybrid double-integrator, an amphibious robot, and a flying-driving drone, showing the practicality of the approach.

IROS Conference 2020 Conference Paper

Sequential Motion Planning for Bipedal Somersault via Flywheel SLIP and Momentum Transmission with Task Space Control

Xiaobin Xiong
Aaron D. Ames

In this paper, we present a sequential motion planning and control method for generating somersaults on bipedal robots. The somersault (backflip or frontflip) is considered as a coupling between an axile hopping motion and a rotational motion about the center of mass of the robot; these are encoded by a hopping Spring-loaded Inverted Pendulum (SLIP) model and the rotation of a Flywheel, respectively. We thus present the Flywheel SLIP model for generating the desired motion on the ground phase. In the flight phase, we present a momentum transmission method to adjust the orientation of the lower body based on the conservation of the centroidal momentum. The generated motion plans are realized on the full-dimensional robot via momentum-included task space control. Finally, the proposed method is implemented on a modified version of the bipedal robot Cassie in simulation wherein multiple somersault motions are generated.

IROS Conference 2019 Conference Paper

Motion Decoupling and Composition via Reduced Order Model optimization for Dynamic Humanoid Walking with CLF-QP based Active Force Control

Xiaobin Xiong
Aaron D. Ames

In this paper, 3D humanoid walking is decoupled into periodic and transitional motion, each of which is decoupled into planar walking in the sagittal and lateral plane. Reduced order models (ROMs), i. e. actuated Spring-loaded Inverted Pendulum (aSLIP) models and Hybrid-Linear Inverted Pendulum (H-LIP) models, are utilized for motion generation on the desired center of mass (COM) dynamics for each type of planar motion. The periodic motion is planned via point foot (underactuated) ROMs for dynamic motion with minimum ankle actuation, while the transitional motion is planned via foot-actuated ROMs for fast and smooth transition. Composition of the planar COM dynamics yields the desired COM dynamics in 3D, which is embedded on the humanoid via control Lyapunov function based Quadratic programs (CLF-QPs). Additionally, the ground reaction force profiles of the aSLIP walking are used as desired references for ground contact forces in the CLF-QPs for smooth domain transitions. The proposed framework is realized on a lower-limb exoskeleton in simulation wherein different walking motions are achieved.

IROS Conference 2019 Conference Paper

Orbit Characterization, Stabilization and Composition on 3D Underactuated Bipedal Walking via Hybrid Passive Linear Inverted Pendulum Model

Xiaobin Xiong
Aaron D. Ames

A Hybrid passive Linear Inverted Pendulum (H-LIP) model is proposed for characterizing, stabilizing and composing periodic orbits for 3D underactuated bipedal walking. Specifically, Period-l (P1) and Period -2 (P2) orbits are geometrically characterized in the state space of the H-LIP. Stepping controllers are designed for global stabilization of the orbits. Valid ranges of the gains and their optimality are derived. The optimal stepping controller is used to create and stabilize the walking of bipedal robots. An actuated Spring-loaded Inverted Pendulum (aSLIP) model and the underactuated robot Cassie are used for illustration. Both the aSLIP walking with PI or P2 orbits and the Cassie walking with all 3D compositions of the PI and P2 orbits can be smoothly generated and stabilized from a stepping-in-place motion. This approach provides a perspective and a methodology towards continuous gait generation and stabilization for 3D underactuated walking robots.

IROS Conference 2018 Conference Paper

Bipedal Hopping: Reduced-Order Model Embedding via Optimization-Based Control

Xiaobin Xiong
Aaron D. Ames

This paper presents the design and validation of controlling hopping on the 3D bipedal robot Cassie. A spring-mass model is identified from the kinematics and compliance of the robot. The spring stiffness and damping are encapsulated by the leg length, thus actuating the leg length can create and control hopping behaviors. Trajectory optimization via direct collocation is performed on the spring-mass model to plan jumping and landing motions. The leg length trajectories are utilized as desired outputs to synthesize a control Lyapunov function based quadratic program (CLF-QP). Centroidal angular momentum, taking as an addition output in the CLF-QP, is also stabilized in the jumping phase to prevent whole body rotation in the underactuated flight phase. The solution to the CLF-QP is a nonlinear feedback control law that achieves dynamic jumping behaviors on bipedal robots with compliance. The framework presented in this paper is verified experimentally on the bipedal robot Cassie.

IROS Conference 2017 Conference Paper

A stability region criterion for flat-footed bipedal walking on deformable granular terrain

Xiaobin Xiong
Aaron D. Ames
Daniel I. Goldman

Achieving stable bipedal robotic walking on deformable terrain is an open and challenging problem at the intersection of robotics and physics. Ground deformation introduces underactuation; uncertainty in terrain dynamics further complicates dynamical modeling and control methods. This work provides a stability criterion for flat-footed bipedal locomotion and allows model-based control methods to function on homogeneous deformable granular terrain (e. g. sand and dirt). By characterizing static reaction forces from granular materials, in conjunction with granular resistive force theory (RFT), we model and validate a static stability region for the center of mass (CoM) projection of a biped on a granular surface. We show that this stability region approximates the admissible Zero Moment Point (ZMP) region for walking, rendering common Linear Inverted Pendulum Model (LIPM) methods valid with our foot placement strategy. By interpreting the stability region as the maximum reaction moment of the terrain, we formulate walking as a hybrid dynamical system and utilize the partial hybrid zero dynamics (PHZD) based methodology to generate walking gaits. Finally, we experimentally validate both the ZMP and PHZD walking gaits on a planar bipedal robot, showing that the stability region criterion permits stable dynamic walking on homogeneous granular terrain.