Whole-Body Model Predictive Control for Biped Locomotion on a Torque-Controlled Humanoid Robot

Dantec, Ewen; Naveau, Maximilien; Fernbach, Pierre; Villa, Nahuel; Saurel, Guilhem; Stasse, Olivier; Taïx, Michel; Mansard, Nicolas

doi:10.1109/humanoids53995.2022.10000129

Cited by 21 publications

(7 citation statements)

References 36 publications

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“…In previous works (e.g., [6], [21], [28]), ℓ(•) contains a cost to stiffly track some user-defined reference swing foot trajectory, limiting the range of motions that the solver can discover. We introduce a novel cost, called ''fly-high'', to replace the classic trajectory tracking cost.…”

Section: A Fly-high Costsmentioning

confidence: 99%

Reference-Free Model Predictive Control for Quadrupedal Locomotion

Lunardi,

Corbères,

Mastalli

et al. 2024

IEEE Access

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Full-dynamics model predictive control (MPC) has recently been applied to quadrupedal locomotion in semi-unstructured environments. These advances have been fueled by the availability of efficient trajectory optimization (TO) algorithms and inexpensive computational power. The main advantages of full-dynamics MPC are (i) enabling complex locomotion manoeuvres, (ii) considering actuation limits, and (iii) improving robot stability. However, to make the TO problem sufficiently simple to be solved at run time, reference swing foot trajectories are usually tracked in the MPC formulation. These trajectories are often computed independently of the motion of the joints, limiting the approach generality and capability. To address this limitation, we present a full-dynamics MPC formulation that does not require reference swing-foot trajectories, featuring a novel cost function targeting swing foot motion and considering environmental information. Removing the need for reference swing foot trajectories, our approach can also automatically adjust footstep locations, as long as the contact surfaces are predefined. We have validated our MPC formulation through simulations and experiments on the ANYmal B robot. Our approach has similar computational efficiency to state-of-the-art formulations, while displaying superior push-recovery capabilities on various terrains.

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Section: A Fly-high Costsmentioning

confidence: 99%

Reference-Free Model Predictive Control for Quadrupedal Locomotion

Lunardi,

Corbères,

Mastalli

et al. 2024

IEEE Access

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“…In addition to model-based methods, spline interpolation can also be used for gait generation, including methods such as cubic and quintic splines [17][18][19][20][21][22]. After gait generation, the Whole Body Control (WBC) and the Model Predictive Control (MPC) methods can be used for robot control [23][24][25][26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Bipedal Robot Gait Generation Using Bessel Interpolation

Wang,

Li,

Kou

et al. 2024

Biomimetics

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This paper introduces a novel approach to bipedal robot gait generation by proposing a higher-order form through the parameter equation of first-order Bessel interpolation. The trajectory planning for the bipedal robot, specifically for stepping up or down stairs, is established based on a three-dimensional interpolation equation. The experimental prototype, Roban, is utilized for the study, and the structural sketch of a single leg is presented. The inverse kinematics expression for the leg is derived using kinematic methods. Employing a position control method, the angle information is transmitted to the robot’s joints, enabling the completion of both downstairs simulation experiments and physical experiments with the Roban prototype. The analysis of the experimental process reveals a noticeable phenomenon of hip and ankle joint tilting in the robot. This observation suggests that low-cost bipedal robots driven by servo motors exhibit low stiffness characteristics in their joints.

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“…While Model Predictive Control (MPC) is not classified as a traditional gait control method, its wide application in bipedal robot gait control is noticeable [12]. Reference [13] proposes a holistic MPC scheme based on differential dynamic programming to tackle the challenges of physical constraints and model discrepancies in bipedal robots.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Agent Reinforcement Learning Method for Omnidirectional Walking of Bipedal Robots

Mou,

Xue,

Liu

et al. 2023

Biomimetics

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Achieving omnidirectional walking for bipedal robots is considered one of the most challenging tasks in robotics technology. Reinforcement learning (RL) methods have proved effective in bipedal walking tasks. However, most existing methods use state machines to switch between multiple policies and achieve omnidirectional gait, which results in shaking during the policy switching process for bipedal robots. To achieve a seamless transition between omnidirectional gait and transient motion for full-size bipedal robots, we propose a novel multi-agent RL method. Firstly, a multi-agent RL algorithm based on the actor–critic framework is designed, and policy entropy is introduced to improve exploration efficiency. By learning agents with parallel initial state distributions, we minimize reliance on gait planner effectiveness in the Robot Operating System (ROS). Additionally, we design a novel heterogeneous policy experience replay mechanism based on Euclidean distance. Secondly, considering the periodicity of bipedal robot walking, we develop a new periodic gait function. Including periodic objectives in the policy can accelerate the convergence speed of training periodic gait functions. Finally, to enhance the robustness of the policy, we construct a novel curriculum learning method by discretizing Gaussian distribution and incorporate it into the robot’s training task. Our method is validated in a simulation environment, and the results show that our method can achieve multiple gaits through a policy network and achieve smooth transitions between different gaits.

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Whole-Body Model Predictive Control for Biped Locomotion on a Torque-Controlled Humanoid Robot

Cited by 21 publications

References 36 publications

Reference-Free Model Predictive Control for Quadrupedal Locomotion

Reference-Free Model Predictive Control for Quadrupedal Locomotion

Bipedal Robot Gait Generation Using Bessel Interpolation

A Multi-Agent Reinforcement Learning Method for Omnidirectional Walking of Bipedal Robots

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