Human Motion Analysis Using Very Few Inertial Measurement Units

Realistic character animation and human motion analysis have become major topics of research. In this doctoral research work, three different aspects of human motion analysis and synthesis have been explored. Firstly, on the level of better management of tens of gigabytes of publicly available human motion capture data sets, a relational database approach has been proposed. We show that organizing motion capture data in a relational database provides several benefits such as centralized access to major freely available mocap data sets, fast search and retrieval of data, annotations based retrieval of contents, entertaining data from non-mocap sensor modalities etc. Moreover, the same idea is also proposed for managing quadruped motion capture data. Secondly, a new method of full body human motion reconstruction using very sparse configuration of sensors is proposed. In this setup, two sensor are attached to the upper extremities and one sensor is attached to the lower trunk. The lower trunk sensor is used to estimate ground contacts, which are later used in the reconstruction process along with the low dimensional inputs from the sensors attached to the upper extremities. The reconstruction results of the proposed method have been compared with the reconstruction results of the existing approaches and it has been observed that the proposed method generates lower average reconstruction errors. Thirdly, in the field of human motion analysis, a novel method of estimation of human soft biometrics such as gender, height, and age from the inertial data of a simple human walk is proposed. The proposed method extracts several features from the time and frequency domains for each individual step. A random forest classifier is fed with the extracted features in order to estimate the soft biometrics of a human. The results of classification have shown that it is possible with a higher accuracy to estimate the gender, height, and age of a human from the inertial data of a single step of his/her walk

In silico case studies of compliant robots: AMARSI deliverable 3.3

In the deliverable 3.2 we presented how the morphological computing ap- proach can significantly facilitate the control strategy in several scenarios, e.g. quadruped locomotion, bipedal locomotion and reaching. In particular, the Kitty experimental platform is an example of the use of morphological computation to allow quadruped locomotion. In this deliverable we continue with the simulation studies on the application of the different morphological computation strategies to control a robotic system

Fault Tolerant Free Gait and Footstep Planning for Hexapod Robot Based on Monte-Carlo Tree

Legged robots can pass through complex field environments by selecting gaits and discrete footholds carefully. Traditional methods plan gait and foothold separately and treat them as the single-step optimal process. However, such processing causes its poor passability in a sparse foothold environment. This paper novelly proposes a coordinative planning method for hexapod robots that regards the planning of gait and foothold as a sequence optimization problem with the consideration of dealing with the harshness of the environment as leg fault. The Monte Carlo tree search algorithm(MCTS) is used to optimize the entire sequence. Two methods, FastMCTS, and SlidingMCTS are proposed to solve some defeats of the standard MCTS applicating in the field of legged robot planning. The proposed planning algorithm combines the fault-tolerant gait method to improve the passability of the algorithm. Finally, compared with other planning methods, experiments on terrains with different densities of footholds and artificially-designed challenging terrain are carried out to verify our methods. All results show that the proposed method dramatically improves the hexapod robot's ability to pass through sparse footholds environment

RLOC: Terrain-Aware Legged Locomotion using Reinforcement Learning and Optimal Control

We present a unified model-based and data-driven approach for quadrupedal planning and control to achieve dynamic locomotion over uneven terrain. We utilize on-board proprioceptive and exteroceptive feedback to map sensory information and desired base velocity commands into footstep plans using a reinforcement learning (RL) policy trained in simulation over a wide range of procedurally generated terrains. When ran online, the system tracks the generated footstep plans using a model-based controller. We evaluate the robustness of our method over a wide variety of complex terrains. It exhibits behaviors which prioritize stability over aggressive locomotion. Additionally, we introduce two ancillary RL policies for corrective whole-body motion tracking and recovery control. These policies account for changes in physical parameters and external perturbations. We train and evaluate our framework on a complex quadrupedal system, ANYmal version B, and demonstrate transferability to a larger and heavier robot, ANYmal C, without requiring retraining.Comment: 19 pages, 15 figures, 6 tables, 1 algorithm, submitted to T-RO; under revie

Inverse Dynamics Trajectory Optimization for Contact-Implicit Model Predictive Control

Robots must make and break contact to interact with the world and perform useful tasks. However, planning and control through contact remains a formidable challenge. In this work, we achieve real-time contact-implicit model predictive control with a surprisingly simple method: inverse dynamics trajectory optimization. While trajectory optimization with inverse dynamics is not new, we introduce a series of incremental innovations that collectively enable fast model predictive control on a variety of challenging manipulation and locomotion tasks. We implement these innovations in an open-source solver, and present a variety of simulation examples to support the effectiveness of the proposed approach. Additionally, we demonstrate contact-implicit model predictive control on hardware at over 100 Hz for a 20 degree-of-freedom bi-manual manipulation task

Design and Data-Driven Identification of a Quadruped Robot

The existence of nonlinearities and the lack of sufficient equations are fundamental challenges in modeling, analyzing, and controlling complex systems. However, recent developments revolutionizing the study of dynamical systems. An emerging method in nonlinear dynamical systems is the Koopman operator theory, which provides us with key advantages in performing the modeling, prediction, and control of nonlinear systems. The linear system representation allows us to leverage linear stability analysis. The first section of this thesis briefly covers the construction of a quadrupedal robot, a sufficiently complex nonlinear dynamical system, for the use of analyzing data-driven modeling techniques. The second section details the physics based dynamic model of this quadruped, using linearized single rigid body dynamics and then we provide the data-driven Koopman models using DMDc. We are able to show that the physics-based model fails to capture the dynamics of the system, whereas the Koopman model can accurately forecast the nonlinear response of the system. Lastly, we propose an experimental framework to obtain a data-driven Koopman model of a quadrupeds leg dynamics over deformable terrains as a switched system. Results show that the Koopman generator has a unique spectrum associated with each terrain, making terrain classification possible, using only proprioceptive sensors. Through the methods presented in this thesis, we are able to show that the data-driven models of dynamical systems using Koopman operator theory can sufficiently approximate the nonlinearities of the system and can accurately predict future trajectories of the system

Online Optimization-based Gait Adaptation of Quadruped Robot Locomotion

Quadruped robots demonstrated extensive capabilities of traversing complex and unstructured environments. Optimization-based techniques gave a relevant impulse to the research on legged locomotion. Indeed, by designing the cost function and the constraints, we can guarantee the feasibility of a motion and impose high-level locomotion tasks, e.g., tracking of a reference velocity. This allows one to have a generic planning approach without the need to tailor a specific motion for each terrain, as in the heuristic case. In this context, Model Predictive Control (MPC) can compensate for model inaccuracies and external disturbances, thanks to the high-frequency replanning. The main objective of this dissertation is to develop a Nonlinear MPC (NMPC)-based locomotion framework for quadruped robots. The aim is to obtain an algorithm which can be extended to different robots and gaits; in addition, I sought to remove some assumptions generally done in the literature, e.g., heuristic reference generator and user-defined gait sequence. The starting point of my work is the definition of the Optimal Control Problem to generate feasible trajectories for the Center of Mass. It is descriptive enough to capture the linear and angular dynamics of the robot as a whole. A simplified model (Single Rigid Body Dynamics model) is used for the system dynamics, while a novel cost term maximizes leg mobility to improve robustness in the presence of nonflat terrain. In addition, to test the approach on the real robot, I dedicated particular effort to implementing both a heuristic reference generator and an interface for the controller, and integrating them into the controller framework developed previously by other team members. As a second contribution of my work, I extended the locomotion framework to deal with a trot gait. In particular, I generalized the reference generator to be based on optimization. Exploiting the Linear Inverted Pendulum model, this new module can deal with the underactuation of the trot when only two legs are in contact with the ground, endowing the NMPC with physically informed reference trajectories to be tracked. In addition, the reference velocities are used to correct the heuristic footholds, obtaining contact locations coherent with the motion of the base, even though they are not directly optimized. The model used by the NMPC receives as input the gait sequence, thus with the last part of my work I developed an online multi-contact planner and integrated it into the MPC framework. Using a machine learning approach, the planner computes the best feasible option, even in complex environments, in a few milliseconds, by ranking online a set of discrete options for footholds, i.e., which leg to move and where to step. To train the network, I designed a novel function, evaluated offline, which considers the value of the cost of the NMPC and robustness/stability metrics for each option. These methods have been validated with simulations and experiments over the three years. I tested the NMPC on the Hydraulically actuated Quadruped robot (HyQ) of the IIT’s Dynamic Legged Systems lab, performing omni-directional motions on flat terrain and stepping on a pallet (both static and relocated during the motion) with a crawl gait. The trajectory replanning is performed at high-frequency, and visual information of the terrain is included to traverse uneven terrain. A Unitree Aliengo quadruped robot is used to execute experiments with the trot gait. The optimization-based reference generator allows the robot to reach a fixed goal and recover from external pushes without modifying the structure of the NMPC. Finally, simulations with the Solo robot are performed to validate the neural network-based contact planning. The robot successfully traverses complex scenarios, e.g., stepping stones, with both walk and trot gaits, choosing the footholds online. The achieved results improved the robustness and the performance of the quadruped locomotion. High-frequency replanning, dealing with a fixed goal, recovering after a push, and the automatic selection of footholds could help the robots to accomplish important tasks for the humans, for example, providing support in a disaster response scenario or inspecting an unknown environment. In the future, the contact planning will be transferred to the real hardware. Possible developments foresee the optimization of the gait timings, i.e., stance and swing duration, and a framework which allows the automatic transition between gaits