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

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

On discrete symmetries of robotics systems: A group-theoretic and data-driven analysis

We present a comprehensive study on discrete morphological symmetries of dynamical systems, which are commonly observed in biological and artificial locomoting systems, such as legged, swimming, and flying animals/robots/virtual characters. These symmetries arise from the presence of one or more planes/axis of symmetry in the system's morphology, resulting in harmonious duplication and distribution of body parts. Significantly, we characterize how morphological symmetries extend to symmetries in the system's dynamics, optimal control policies, and in all proprioceptive and exteroceptive measurements related to the system's dynamics evolution. In the context of data-driven methods, symmetry represents an inductive bias that justifies the use of data augmentation or symmetric function approximators. To tackle this, we present a theoretical and practical framework for identifying the system's morphological symmetry group \G and characterizing the symmetries in proprioceptive and exteroceptive data measurements. We then exploit these symmetries using data augmentation and \G-equivariant neural networks. Our experiments on both synthetic and real-world applications provide empirical evidence of the advantageous outcomes resulting from the exploitation of these symmetries, including improved sample efficiency, enhanced generalization, and reduction of trainable parameters.Comment: 8 pages, 4 figures, 7 optional appendix pages, 4 appendix figure

Versatile Reactive Bipedal Locomotion Planning Through Hierarchical Optimization

© 2019 IEEE. When experiencing disturbances during locomotion, human beings use several strategies to maintain balance, e.g. changing posture, modulating step frequency and location. However, when it comes to the gait generation for humanoid robots, modifying step time or body posture in real time introduces nonlinearities in the walking dynamics, thus increases the complexity of the planning. In this paper, we propose a two-layer hierarchical optimization framework to address this issue and provide the humanoids with the abilities of step time and step location adjustment, Center of Mass (CoM) height variation and angular momentum adaptation. In the first layer, times and locations of consecutive two steps are modulated online based on the current CoM state using the Linear Inverted Pendulum Model. By introducing new optimization variables to substitute the hyperbolic functions of step time, the derivatives of the objective function and feasibility constraints are analytically derived, thus reduces the computational cost. Then, taking the generated horizontal CoM trajectory, step times and step locations as inputs, CoM height and angular momentum changes are optimized by the second layer nonlinear model predictive control. This whole procedure will be repeated until the termination condition is met. The improved recovery capability under external disturbances is validated in simulation studies

Morphological symmetries in robot learning

This work studies the impact of morphological symmetries in learning applications in robotics. Morphological symmetries are a predominant feature in both biological and robotic systems, arising from the presence of planes/axis of symmetry in the system's morphology. This results in harmonious duplication and distribution of body parts (e.g., humans' sagittal/left-right symmetry). Morphological symmetries become a significant learning prior as they extend to symmetries in the system's dynamics, optimal control policies, and in all proprioceptive and exteroceptive measurements, related to the system's dynamics evolution \cite{ordonez2023discrete}. Exploiting these symmetries in learning applications offers several advantageous outcomes, such as the use of data augmentation to mitigate the cost and challenges of data collection, or the use of equivariant/invariant function approximation models (e.g., neural networks) to improve sample efficiency and generalization, while reducing the number of trainable parameters. Lastly, we provide a video presentation and an open access repository reproducing our experiments and allowing for rapid prototyping in robot learning applications exploiting morphological symmetries.This work is supported by the Spanish government with the project MoHuCo PID2020-120049RB-I00 and the ERA-Net Chistera project IPALM PCI2019-103386.Peer ReviewedPostprint (published version

Planning and Control Strategies for Motion and Interaction of the Humanoid Robot COMAN+

Despite the majority of robotic platforms are still confined in controlled environments such as factories, thanks to the ever-increasing level of autonomy and the progress on human-robot interaction, robots are starting to be employed for different operations, expanding their focus from uniquely industrial to more diversified scenarios. Humanoid research seeks to obtain the versatility and dexterity of robots capable of mimicking human motion in any environment. With the aim of operating side-to-side with humans, they should be able to carry out complex tasks without posing a threat during operations. In this regard, locomotion, physical interaction with the environment and safety are three essential skills to develop for a biped. Concerning the higher behavioural level of a humanoid, this thesis addresses both ad-hoc movements generated for specific physical interaction tasks and cyclic movements for locomotion. While belonging to the same category and sharing some of the theoretical obstacles, these actions require different approaches: a general high-level task is composed of specific movements that depend on the environment and the nature of the task itself, while regular locomotion involves the generation of periodic trajectories of the limbs. Separate planning and control architectures targeting these aspects of biped motion are designed and developed both from a theoretical and a practical standpoint, demonstrating their efficacy on the new humanoid robot COMAN+, built at Istituto Italiano di Tecnologia. The problem of interaction has been tackled by mimicking the intrinsic elasticity of human muscles, integrating active compliant controllers. However, while state-of-the-art robots may be endowed with compliant architectures, not many can withstand potential system failures that could compromise the safety of a human interacting with the robot. This thesis proposes an implementation of such low-level controller that guarantees a fail-safe behaviour, removing the threat that a humanoid robot could pose if a system failure occurred

Cooperative Control Design for Robot-Assisted Balance During Gait

Avoiding falls is a challenge for many persons in aging societies, and balance dysfunction is a major risk factor. Robotic solutions to assist human gait, however, focus on average kinematics, and less on instantaneous balance reactions. We propose a controller that only intervenes when needed, and that avoids stability issues when interacting with humans: Assistance is triggered only when balance is lost, and this action is purely feed-forward. Experiments show that subjects who start falling during gait can be uprighted by such feed-forward assistive force

Orientation-Aware Model Predictive Control with Footstep Adaptation for Dynamic Humanoid Walking

This paper proposes a novel orientation-aware model predictive control (MPC) for dynamic humanoid walking that can plan footstep locations online. Instead of a point-mass model, this work uses the augmented single rigid body model (aSRBM) to enable the MPC to leverage orientation dynamics and stepping strategy within a unified optimization framework. With the footstep location as part of the decision variables in the aSRBM, the MPC can reason about stepping within the kinematic constraints. A task-space controller (TSC) tracks the body pose and swing leg references output from the MPC, while exploiting the full-order dynamics of the humanoid. The proposed control framework is suitable for real-time applications since both MPC and TSC are formulated as quadratic programs. Simulation investigations show that the orientation-aware MPC-based framework is more robust against external torque disturbance compared to state-of-the-art controllers using the point mass model, especially when the torso undergoes large angular excursion. The same control framework can also enable the MIT Humanoid to overcome uneven terrains, such as traversing a wave field