Enhancing Model-Based Step Adaptation for Push Recovery through Reinforcement Learning of Step Timing and Region

This paper introduces a new approach to enhance the robustness of humanoid walking under strong perturbations, such as substantial pushes. Effective recovery from external disturbances requires bipedal robots to dynamically adjust their stepping strategies, including footstep positions and timing. Unlike most advanced walking controllers that restrict footstep locations to a predefined convex region, substantially limiting recoverable disturbances, our method leverages reinforcement learning to dynamically adjust the permissible footstep region, expanding it to a larger, effectively non-convex area and allowing cross-over stepping, which is crucial for counteracting large lateral pushes. Additionally, our method adapts footstep timing in real time to further extend the range of recoverable disturbances. Based on these adjustments, feasible footstep positions and DCM trajectory are planned by solving a QP. Finally, we employ a DCM controller and an inverse dynamics whole-body control framework to ensure the robot effectively follows the trajectory.

Shared Autonomy via Variable Impedance Control and Virtual Potential Fields for Encoding Human Demonstration

This article introduces a framework for complex human-robot collaboration tasks, such as the co-manufacturing of furniture. For these tasks, it is essential to encode tasks from human demonstration and reproduce these skills in a compliant and safe manner. Therefore, two key components are addressed in this work: motion generation and shared autonomy. We propose a motion generator based on a time-invariant potential field, capable of encoding wrench profiles, complex and closed-loop trajectories, and additionally incorporates obstacle avoidance. Additionally, the paper addresses shared autonomy (SA) which enables synergetic collaboration between human operators and robots by dynamically allocating authority. Variable impedance control (VIC) and force control are employed, where impedance and wrench are adapted based on the human-robot autonomy factor derived from interaction forces. System passivity is ensured by an energy-tank based task passivation strategy. The framework's efficacy is validated through simulations and an experimental study employing a Franka Emika Research 3 robot.

Observer-based Controller Design for Oscillation Damping of a Novel Suspended Underactuated Aerial Platform

In this work, we present a novel actuation strategy for a suspended aerial platform. By utilizing an underactuation approach, we demonstrate the successful oscillation damping of the proposed platform, modeled as a spherical double pendulum. A state estimator is designed in order to obtain the deflection angles of the platform, which uses only onboard IMU measurements. The state estimator is an extended Kalman filter (EKF) with intermittent measurements obtained at different frequencies. An optimal state feedback controller and a PD+ controller are designed in order to dampen the oscillations of the platform in the joint space and task space respectively. The proposed underactuated platform is found to be more energy-efficient than an omnidirectional platform and requires fewer actuators. The effectiveness of our proposed system is validated using both simulations and experimental studies.

Hierarchical Whole-body Control of the cable-Suspended Aerial Manipulator endowed with Winch-based Actuation

During operation, aerial manipulation systems are affected by various disturbances. Among them is a gravitational torque caused by the weight of the robotic arm. Common propeller-based actuation is ineffective against such disturbances because of possible overheating and high power consumption. To overcome this issue, in this paper we propose a winchbased actuation for the crane-stationed cable-suspended aerial manipulator. Three winch-controlled suspension rigging cables produce a desired cable tension distribution to generate a wrench that reduces the effect of gravitational torque. In order to coordinate the robotic arm and the winch-based actuation, a model-based hierarchical whole-body controller is adapted. It resolves two tasks: keeping the robotic arm end-effector at the desired pose and shifting the system center of mass in the location with zero gravitational torque. The performance of the introduced actuation system as well as control strategy is validated through experimental studies.

EigenMPC: An Eigenmanifold-Inspired Model-Predictive Control Framework for Exciting Efficient Oscillations in Mechanical Systems

This paper proposes a Nonlinear Model-Predictive Control (NMPC) method capable of finding and converging to energy-efficient regular oscillations, which require no control action to be sustained. The approach builds up on the recently developed Eigenmanifold theory, which defines the sets of line-shaped oscillations of a robot as an invariant two-dimensional submanifold of its state space. By defining the control problem as a nonlinear program (NLP), the controller is able to deal with constraints in the state and control variables and be energy-efficient not only in its final trajectory but also during the convergence phase. An initial implementation of this approach is proposed, analyzed, and tested in simulation.

Optimal Oscillation Damping Control of cable-Suspended Aerial Manipulator with a Single IMU Sensor

This paper presents a design of oscillation damping control for the cable-Suspended Aerial Manipulator (SAM). The SAM is modeled as a double pendulum, and it can generate a body wrench as a control action. The main challenge is the fact that there is only one onboard IMU sensor which does not provide full information on the system state. To overcome this difficulty, we design a controller motivated by a simplified SAM model. The proposed controller is very simple yet robust to model uncertainties. Moreover, we propose a gain tuning rule by formulating the proposed controller in the form of output feedback linear quadratic regulation problem. Consequently, it is possible to quickly dampen oscillations with minimal energy consumption. The proposed approach is validated through simulations and experiments.

Whole-Body Bilateral Teleoperation of a Redundant Aerial Manipulator

Attaching a robotic manipulator to a flying base allows for significant improvements in the reachability and versatility of manipulation tasks. In order to explore such systems while taking advantage of human capabilities in terms of perception and cognition, bilateral teleoperation arises as a reasonable solution. However, since most telemanipulation tasks require visual feedback in addition to the haptic one, real-time (task-dependent) positioning of a video camera, which is usually attached to the flying base, becomes an additional objective to be fulfilled. Since the flying base is part of the kinematic structure of the robot, if proper care is not taken, moving the video camera could undesirably disturb the end-effector motion. For that reason, the necessity of controlling the base position in the null space of the manipulation task arises. In order to provide the operator with meaningful information about the limits of the allowed motions in the null space, this paper presents a novel haptic concept called Null-Space Wall. In addition, a framework to allow stable bilateral teleoperation of both tasks is presented. Numerical simulation data confirm that the proposed framework is able to keep the system passive while allowing the operator to perform time-delayed telemanipulation and command the base to a task-dependent optimal pose.

Multi-DoF Time Domain Passivity Approach Based Drift Compensation for Telemanipulation

When, in addition to stability, position synchronization is also desired in bilateral teleoperation, Time Domain Passivity Approach (TDPA) alone might not be able to fulfill the desired objective. This is due to an undesired effect caused by admittance type passivity controllers, namely position drift. Previous works focused on developing TDPA-based drift compensation methods to solve this issue. It was shown that, in addition to reducing drift, one of the proposed methods was able to keep the force signals within their normal range, guaranteeing the safety of the task. However, no multi-DoF treatment of those approaches has been addressed. In that scope, this paper focuses on providing an extension of previous TDPA-based approaches to multi-DoF Cartesian-space teleoperation. An analysis of the convergence properties of the presented method is also provided. In addition, its applicability to multi-DoF devices is shown through hardware experiments and numerical simulation with round-trip time delays up to 700 ms.

Model-free Friction Observers for Flexible Joint Robots with Torque Measurements

This paper tackles a friction compensation problem without using a friction model. The unique feature of the proposed friction observer is that the nominal motor-side signal is fed back into the controller instead of the measured signal. By doing so, asymptotic stability and passivity of the controller are maintained. Another advantage of the proposed observer is that it provides a clear understanding for the stiction compensation which is hard to be captured in model-free approaches. This allows to design observers that do not overcompensate for the stiction. The proposed scheme is validated through simulations and experiments.

A Passivity-based Nonlinear Admittance Control with Application to Powered Upper-limb Control under Unknown Environmental Interactions

This paper presents an admittance controller based on the passivity theory for a powered upper-limb exoskeleton robot which is governed by the nonlinear equation of motion. Passivity allows us to include a human operator and environmental interaction in the control loop. The robot interacts with the human operator via F/T sensor and interacts with the environment mainly via end-effectors. Although the environmental interaction cannot be detected by any sensors (hence unknown), passivity allows us to have natural interaction. An analysis shows that the behavior of the actual system mimics that of a nominal model as the control gain goes to infinity, which implies that the proposed approach is an admittance controller. However, because the control gain cannot grow infinitely in practice, the performance limitation according to the achievable control gain is also analyzed. The result of this analysis indicates that the performance in the sense of infinite norm increases linearly with the control gain. In the experiments, the proposed properties were verified using 1 degree-of-freedom testbench, and an actual powered upper-limb exoskeleton was used to lift and maneuver the unknown payload.