Practical considerations in using inverse dynamics on a humanoid robot: torque tracking, sensor fusion and Cartesian control laws

Although considering dynamics in the control of humanoid robots can improve tracking and compliance in agile tasks, it requires local and global states of the system, precise torque control and proper modeling. In this paper we discuss practical issues to implement inverse dynamics on a torque controlled robot. By modeling electrical actuators off-line, inverting such model and estimating the friction on-line, a high bandwidth torque controller is implemented. In addition, a cascade of optimization problems to fuse all the sensory data coming from IMU, joint encoders and contact force sensors estimate the robot's global state robustly. Our estimation builds the kinematic chain of the legs from the center of pressure which is more robust in case of slight slippage, tilting or rolling of the feet. Thanks to precise and fast torque control, robust state estimation and optimization-based whole body inverse dynamics, the real robot can keep balance with very small stiffness and damping in Cartesian space. It can also recover from strong pushes and perform dexterous tasks. The highly compliant and stable performance is based on pure torque control, without any joint damping or position/velocity tracking.

Modeling robot geometries like molecules, application to fast multi-contact posture planning for humanoids

Salman Faraji, Auke Ijspeert

Traditional joint-space models used to describe equations of motion for humanoid robots offer nice properties linked directly to the way these robots are built. However, from a computational point of view and convergence properties, these models are not the fastest when used in planning optimizations. In this paper, inspired by Cartesian coordinates used to model molecular structures, we propose a new modeling technique for humanoid robots. We represent robot segments by vectors and derive equations of motion for the full body. Using this methodology in a complex task of multi-contact posture planning with minimal joint torques, we set up optimization problems and analyze the performance. We demonstrate that compared to joint-space models that get trapped in local minima, the proposed vector-based model offers much faster computational speed and a suboptimal but unique final solution. The underlying principle lies in reducing the nonlinearity and exploiting the sparsity in the problem structure. Apart from the specific case study of posture optimization, these principles can make the proposed technique a promising candidate for many other optimization-based complex tasks in robotics.

Ieee-Inst Electrical Electronics Engineers Inc2018

Rich and Robust Bio-Inspired Locomotion Control for Humanoid Robots

Nicolas Benoît Dominique Van der Noot

Bipedal locomotion is a challenging task in the sense that it requires to maintain dynamic balance while steering the gait in potentially complex environments. Yet, humans usually manage to move without any apparent difficulty, even on rough terrains. This requires a complex control scheme which is far from being understood. In this thesis, we take inspiration from the impressive human walking capabilities to design neuromuscular controllers for humanoid robots. More precisely, we control the robot motors to reproduce the action of virtual muscles commanded by stimulations (i.e. neural signals), similarly to what is done during human locomotion. Because the human neural circuitry commanding these muscles is not completely known, we make hypotheses about this control scheme to simplify it and progressively refine the corresponding rules. This thesis thus aims at developing new walking algorithms for humanoid robots in order to obtain fast, human-like and energetically efficient gaits. In particular, gait robustness and richness are two key aspects of this work. In other words, the gaits developed in the thesis can be steered by an external operator, while being resistant to external perturbations. This is mainly tested during blind walking experiments on COMAN, a 95 cm tall humanoid robot. Yet, the proposed controllers can be adapted to other humanoid robots. In the beginning of this thesis, we adapt and port an existing reflex-based neuromuscular model to the real COMAN platform. When tested in a 2D simulation environment, this model was capable of reproducing stable human-like locomotion. By porting it to real hardware, we show that these neuromuscular controllers are viable solutions to develop new controllers for robotics locomotion. Starting from this reflex-based model, we progressively iterate and transform the stimulation rules to add new features. In particular, gait modulation is obtained with the inclusion of a central pattern generator (CPG), a neural circuit capable of producing rhythmic patterns of neural activity without receiving rhythmic inputs. Using this CPG, the 2D walker controllers are incremented to generate gaits across a range of forward speeds close to the normal human one. By using a similar control method, we also obtain 2D running gaits whose speed can be controlled by a human operator. The walking controllers are later extended to 3D scenarios (i.e. no motion constraint) with the capability to adapt both the forward speed and the heading direction (including steering curvature). In parallel, we also develop a method to automatically learn stimulation networks for a given task and we study how flexible feet affect the gait in terms of robustness and energy efficiency. In sum, we develop neuromuscular controllers generating human-like gaits with steering capabilities. These controllers recruit three main components: (i) virtual muscles generating torque references at the joint level, (ii) neural signals commanding these muscles with reflexes and CPG signals, and (iii) higher level commands controlling speed and heading. Interestingly, these developments target humanoid robots locomotion but can also be used to better understand human locomotion. In particular, the recruitment of a CPG during human locomotion is still a matter open to debate. This question can thus benefit from the experiments performed in this thesis.

EPFL2017

Spine Controller for a Sprawling Posture Robot

Tomislav Horvat, Auke Ijspeert, Kamilo Andres Melo Becerra

More and more biologically inspired robots present spines with multiples degrees of freedom to resemble their biological counterparts. The function of the spine on both animal and robot can play an important role in locomotion. If it has enough degrees of freedom to allow independent control of both shoulder and pelvic girdle (i.e., the points of attachment of body and limb), it can be used to extend the reach of the legs or to increase turning capabilities. In this letter, we present the spine controller for a sprawling quadrupedal robot. The controller achieves a synchronization between spine and legs and allows a precise control of the girdle positions by solving the inverse kinematics of the spine. The controller is validated in simulation and experiments with the real robot showing an ease in the control of the locomotion and achieving a very good tracking between the girdle trajectories with a very small, noncumulative error (less than 6% of the inter-girdle distance). This controller enables both the human operation and the implementation of autonomous path planning and navigation algorithms for these type of amphibious robots, making them more suitable for scenarios with limited maneuverability such as inside pipes or in collapsed buildings.

2017

Rich and Robust Bio-Inspired Locomotion Control for Humanoid Robots

Nicolas Benoît Dominique Van der Noot

EPFL2017