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Mohammadreza Kasaei

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Subject Walking and Push Recovery in Humanoid Robots by Learning from Past Experiments
Advisor Nuno Lau, Artur Pereira
Group Intelligent Robotics and Systems
Status PhD student
Starts 2015/10/01
Ends 2021/01/31
Country Iran
Projects
Past projects
Events CLAWAR'2017, ICARSC'2017, Robot'2017
Proposals
Personal page

About Me

Mohammdreza Kasaei joined the IEETA and IRIS Labs as a Ph.D. student in October 2015, to work on push recovery on humanoid robots using optimal control and machine learning approaches under supervision of Nuno Lau and Artur Pereira. During his Ph.D., Mohammadreza is working on robust walking with push recovery capability in humanoid robots by learning from past experiments. Specifically, Mohammadreza and his supervisors intend to propose a tight coupling between analytical control approaches and machine learning approaches in autonomous humanoid robots.

Before his Ph.D., Mohammadreza completed his Master Degree in Computer Engineering field of Computer Architecture at the University of Isfahan. His full list of publications and corresponding BibTeX files can be found on his Google scholar account.

Latest News


  • April 2020: I have presented my paper at ICARSC2020.
  • March 2020: Our paper A Robust Model-Based Biped Locomotion Framework Based on Three-Mass Model: From Planning to Control got accepted at ICARSC2020.
  • February 2020: Our hierarchical walk engine has been successfully ported on a simulated COMAN humanoid robot. A video of this simulation is avalable online here!!!
  • January 2020: Proximal Policy Optimization(PPO) approach is used on top of analytical control approach to improve the stability of a simulated humanoid robot while it is walking. The intersting results will be published soon.
  • November 2019: I have presented my paper at IROS2019.

Research

My research interests are in the field of robotics, optimal control, artificial intelligence, system design, hardware design and also in computer algorithms including machine vision and signal processing. I have passed some advanced courses through graduate studies on these areas and have utilized them in several practical projects. My research is summarized by the following projects:


‡ A Robust Model-Based Biped Locomotion Framework Based on Three-Mass Model: From Planning to Control

Biped robots are inherently unstable because of their complex kinematics as well as dynamics. Despite types of research in developing biped locomotion, the performance of biped locomotion is still far from the expectations. This paper proposes a model-based framework to generate stable biped locomotion. The core of this framework is an abstract dynamics model which is composed of three masses to consider the dynamics of stance leg, torso and swing leg for minimizing the tracking problems. According to this dynamics model, we propose a modular walking reference trajectories planner which takes into account obstacles to plan all the references. Moreover, this dynamics model is used to formulate the controller as a Model Predictive Control (MPC) scheme which can consider some constraints in the states of the system, inputs, outputs and also mixed input-output. The performance and the robustness of the proposed framework are validated by performing several simulations using MATLAB. The simulation results show that the proposed framework is capable of generating the biped locomotion robustly.

• Publications:
¤ ICARSC2020
• Videos:
¤ Demo1: Path planning scenario scenario

‡ A Hierarchical Framework to Generate Robust Biped Locomotion Based on Divergent Component of Motion

To get out of the laboratory and work in a real environment, humanoids robots must be able to keep their stability under harsh conditions. Since humanoid robots have similar kinematic to a human, humans expect these robots to be robustly capable of stabilizing even in a challenging situation as when a severe push is applied. This paper presents a robust walking framework which not only takes into account the traditional push recovery approaches (e.g., ankle, hip and step strategies) but also uses the concept of Divergent Component of the Motion (DCM) to adjust next step timing and location. The control core of the proposed framework is composed of a Linear-Quadratic-Gaussian (LQG) controller and two proportional controllers. In this framework, the LQG controller tries to track the reference trajectories and the proportional controllers are designed to adjust the next step timing and location that allow the robot to recover from a severe push. The robustness and the performance of the proposed framework have been validated by performing a set of simulations, including walking and push recovery, using MATLAB. The simulation results verified that the proposed framework is capable of providing a robust walking even in very challenging situations.

• Publications:
¤ To appear in Advanced Robotics

‡ A Robust Closed-Loop Biped Locomotion Planner Based on Time Varying Model Predictive Control

Developing robust locomotion for humanoid robots is a complex task due to the unstable nature of these robots and also to the unpredictability of the terrain. A robust locomotion planner is one of the fundamental components for generating stable biped locomotion. This paper presents an optimal closed-loop biped locomotion planner which can plan reference trajectories even in challenging conditions. The proposed planner is designed based on a Time-Varying Model Predictive Control (TVMPC) scheme to be able to consider some constraints in the states, inputs and outputs of the system and also mixed input-output. Moreover, the proposed planner takes into account the vertical motion of the Center of Mass (COM) to generate walking with mostly stretched knees which is more human-like. Additionally, the planner uses the concept of Divergent Component of Motion (DCM) to modify the reference ZMP online to improve the withstanding level of the robot in the presence of severe disturbances. The performance and also the robustness of the proposed planner are validated by performing several simulations using MATLAB. The simulation results show that the proposed planner is capable of generating the biped locomotion robustly.

• Publications:
¤ arxiv.org
• Videos:
¤ Demo1: Diagonal walking on a flat terrain scenario
¤ Demo2: Stair climbing scenario

‡ A Fast and Stable Omnidirectional Walking Engine for the Nao Humanoid Robot

This paper proposes a framework designed to generate a closed-loop walking engine for a humanoid robot. In particular, the core of this framework is an abstract dynamics model which is composed of two masses that represent the lower and the upper body of a humanoid robot. Moreover, according to the proposed dynamics model, the low-level controller is formulated as a LinearQuadratic-Gaussian (LQG) controller that is able to robustly track the desired trajectories. Besides, this framework is fully parametric which allows using an optimization algorithm to find the optimum parameters. To examine the performance of the proposed framework, a set of simulation using a simulated Nao robot in the RoboCup 3D simulation environment has been carried out. Simulation results show that the proposed framework is capable of providing fast and reliable omnidirectional walking. After optimizing the parameters using genetic algorithm (GA), the maximum forward walking velocity that we have achieved was 80:5cm=s.

• Publications:
¤ Robot World Cup 2019
• Videos:
¤ Demo1: Omnidirectional walking scenario
¤ Demo2: Maximum walking speed scenario

‡ A Robust Biped Locomotion Based on Linear-Quadratic-Gaussian Controller and Divergent Component of Motion

Generating robust locomotion for a humanoid robot in the presence of disturbances is difficult because of its high number of degrees of freedom and its unstable nature. In this paper, we used the concept of Divergent Component of Motion (DCM) and propose an optimal closed-loop controller based on Linear-Quadratic-Gaussian to generate a robust and stable walking for humanoid robots. The biped robot dynamics has been approximated using the Linear Inverted Pendulum Model (LIPM). Moreover, we propose a controller to adjust the landing location of the swing leg to increase the withstanding level of the robot against a severe external push. The performance and also the robustness of the proposed controller is analyzed and verified by performing a set of simulations using MATLAB. The simulation results showed that the proposed controller is capable of providing a robust walking even in the presence of disturbances and in challenging situations.

• Publications:
¤ IROS 2019

‡A Model-Based Balance Stabilization System for Biped Robot

This paper presents a model-based balance stabilization system which takes into account not only the stable part of COM dynamics but also the unstable part. In this system, the overall dynamics of a humanoid robot is approximated using a Linear Inverted Pendulum Plus Flywheel Model (LIPPFM). Moreover, Divergent Component of Motion (DCM) is used to define when and where a robot should take a step to prevent falling. The proposed system has been successfully tested by performing several simulations using MATLAB. The simulation results show this system is capable of stabilizing the balance of the robot in various conditions.

• Publications:
¤ WS-IROS2018

‡ A Model-Based Biped Walking Controller Based on Divergent Component of Motion

Biped robots have high degrees of freedom and they are naturally unstable, hence design and develop a reliable walking controller is a complex subject which is one of the interesting topics in the robotic community. In this paper, we proposed a model-based walking controller which is able to negate the effect of external impacts not only by applying compensating torques but also by adjusting the landing location of swing leg. This controller composed of two levels of control which takes into account the stable and unstable dynamics parts of center of mass (COM). In the proposed controller, the overall dynamics of a humanoid robot is approximated using an enhanced version of Linear Inverted Pendulum Plus Flywheel Model (ELIPPFM) and according to this dynamics model, an optimal controller is designed to track the reference trajectories. Moreover, Divergent Component of Motion (DCM) is used to define when and where a robot should take a step to prevent falling. The proposed controller has been successfully tested by performing several simulations using MATLAB. The results showed that the proposed controller is capable of controlling the balance of a simulated robot in presence of severe disturbances.

• Publications:
¤ ICARSC2019

‡ Comparison Study of Well-Known Inverted Pendulum Models for Balance Recovery in Humanoid Robot

Bipedal robots are essentially unstable because of their complex kinematics as well as high dimensional state space dynamics, hence control and generation of stable walking is a complex subject and still one of the active topics in the robotic community. Nowadays, there are many humanoids performing stable walking, but fewer show effective push recovery under pushes. In this paper, we firstly review more common used abstract dynamics models for a humanoid robot which are based on the inverted pendulum and show how these models can be used to provide walking for a humanoid robot and also how a hierarchical control structure could fade the complexities of a humanoid walking. Secondly, the reviewed models are compared together not only in an analytical manner but also by performing several numerical simulations in a push recovery scenario using MATLAB. These theoretical and simulation studies quantitatively compare these models regarding regaining balance. The results showed that the enhanced version of Linear Inverted Pendulum Plus Flywheel is the ablest dynamics model to regain the stability of the robot even in very challenging situations.

• Publications:
¤ MAPi Seminar(MAPiS)
¤ arxiv.org


‡ An optimal closed-loop framework to develop stable walking for humanoid robot

Bipedal robots are essentially unstable because of their complex kinematics as well as high dimensional state space dynamics, hence control and generation of stable walking is a complex subject that is still one of the active topics in the robotic community. This paper proposes a closed-loop model-based walk engine which takes into account push recovery strategies. In this paper, Linear Inverted Pendulum Plus Flywheel Model (LIPPFM) is extended and used to approximate the overall dynamics of a humanoid robot. We extended this model by releasing the height constraint of the center of mass (COM) as well as by considering the mass of pendulum to increase the accuracy of the model. In this framework, a step is composed of a double support phase in addition to a single support phase. Moreover, ZMP and reference trajectory generators are formulated based on the input parameters and tracking problem are formulated as a finite-time horizon linear quadratic regulator (LQR) problem. The proposed framework has been successfully tested by performing several simulations using MATLAB. The simulation results show this framework is capable to provide stable walking on an uneven terrain.

• Publications:
¤ ICARSC2018

‡ A Hybrid ZMP-CPG Based Walk Engine for Biped Robots

Developing an optimized omnidirectional walking for biped robots is a challenging task due to their complex dynamics and kinematics. This paper proposes a hierarchical walk engine structure to generate fast and stable walking. In particular, this structure provides a closed-loop CPG-based omnidirectional walking that takes into account two human-inspired push recovery strategies. In addition, this structure is fully parametric and allows using a policy search algorithm to find the optimum parameters for the walking. To show the performance of the proposed structure, a set of experiments on a simulated NAO robot has been carried out. Experimental results demonstrate that the proposed structure is able to generate fast and stable omnidirectional walking. The maximum speed of forward walking that we have achieved was 59cm=s.

• Publications:
¤ ROBOT2017
• Videos:
¤ Demo1: Omnidirectional walking scenario
¤ Demo1: Maximum walking speed scenario


‡ A Reliable Hierarchical Omnidirectional Walking Engine for A Bipedal Robot by using The Enhanced LIP Plus Flywheel

According to the similarity in kinematic architecture, bipedal robots are the most appropriate type of robot to operate in humanoid environments. Most of the humanoid robots have more than 20 degrees of freedom (DoF), therefore they have complex kinematics and dynamics. Due to these complexities, developing a stable walking engine is a difficult subject which is still one of the main challenges. In this paper, a hierarchical walking engine is presented which tries to fade the complexities and increases the flexibility and portability. To generate the reference trajectories of walking, Linear Inverted Pendulum Plus Flywheel Model is used. We enhanced this model to release the height constraint of the Center of Mass (CoM). This enhancement not only provides more natural motion but also it provides larger stride. The reliability of the proposed structure is verified through real experiments for an 110cm bipedal robot. The experimental results show the performance of this controller to keep robot’s stability during walking. The average speed of walking that we have achieved was 20cm=sec.

• Publications:
¤ CLAWAR2017
• Videos:
¤ Demo1: Real Robot Experiment


‡ A Reliable Model-Based Walking Engine with Push Recovery Capability

According to the similarity in kinematic architecture, bipedal robots are the most appropriate type of robot to operate in humanoid environments. Most of the humanoid robots have more than 20 degrees of freedom (DoF), therefore they have complex kinematics and dynamics. Due to these complexities, developing a stable walking engine is a difficult subject which is still one of the main challenges. In this paper, a hierarchical walking engine is presented which tries to fade the complexities and increases the flexibility and portability. To generate the reference trajectories of walking, Linear Inverted Pendulum Plus Flywheel Model is used. We enhanced this model to release the height constraint of the Center of Mass (CoM). This enhancement not only provides more natural motion but also it provides larger stride. The reliability of the proposed structure is verified through real experiments for an 110cm bipedal robot. The experimental results show the performance of this controller to keep robot’s stability during walking. The average speed of walking that we have achieved was 20cm=sec.

• Publications:
¤ ICARSC2017
• Videos:
¤ Demo1: Real Robot Experiment


‡ How to Select A Suitable Action Against Strong Pushes in Adult-Size Humanoid Robot: Learning From Past Experiences

Avoiding a fall after strong collisions between two players is an important capability for an adult-size humanoid robot. Particularly in the RoboCup competitions, matches are really competitive and collisions between players are occurred frequently. In the adult-size humanoid league, robots are tall and heavy. Whenever robots contact each other during moving, several unpredicted non-linear forces are entered to the robots. As a consequence, the stability of robots goes out of control and they fall down. In order to maintain and recover balance of an adult-size humanoid robot against external disturbances, a Neural Network is used for learning from past experiments to reduce the effect of disturbances forces by providing proper step sizes and joint angles to the robot. In our approach, the robot’s controller is learned using several empirical experiments and tested on a real adult-size humanoid robot namely Ariana from BehRobot humanoid team. Experiments demonstrate after receiving strong pushes during walking, Ariana can efficiently recover its stability in the real environment.

• Publications:
¤ ICARSC2016
• Videos:
¤ Demo1: Real Robot Experiment



Publications

Articles in conference proceedings