Teleoperated humanoid robot systems have made substantial advancements in recent years, offering a physical avatar that harnesses human skills and decision-making while safeguarding users from hazardous environments. However, current telelocomotion interfaces often fail to accurately represent the robot's environment, limiting the user's ability to effectively navigate the robot through unstructured terrain. This paper presents an initial telelocomotion framework that integrates the ForceBot locomotion interface with the small-sized humanoid robot, HECTOR V2. The framework utilizes ForceBot to simulate walking motion and estimate the user's Center of Mass (CoM) trajectory, which serves as a tracking reference for the robot. On the robot side, a model predictive control (MPC) approach, based on a reduced-order single rigid body model, is employed to track the user's scaled trajectory. We present experimental results on ForceBot's CoM estimation and the robot's tracking performance, demonstrating the feasibility of this approach.

In this work, the Divergent Component of Motion (DCM) method is expanded to include angular coordinates for the first time. This work introduces the idea of spatial DCM, which adds an angular objective to the existing linear DCM theory. To incorporate the angular component into the framework, a discussion is provided on extending beyond the linear motion of the Linear Inverted Pendulum model (LIPM) towards the Single Rigid Body model (SRBM) for DCM. This work presents the angular DCM theory for a 1D rotation, simplifying the SRBM rotational dynamics to a flywheel to satisfy necessary linearity constraints. The 1D angular DCM is mathematically identical to the linear DCM and defined as an angle which is ahead of the current body rotation based on the angular velocity. This theory is combined into a 3D linear and 1D angular DCM framework, with discussion on the feasibility of simultaneously achieving both sets of objectives. A simulation in MATLAB and hardware results on the TORO humanoid are presented to validate the framework's performance.

Many state-of-the-art robotic applications utilize series elastic actuators (SEAs) with closed-loop force control to achieve complex tasks such as walking, lifting, and manipulation. Model-free PID control methods are more prone to instability due to nonlinearities in the SEA where cascaded model-based robust controllers can remove these effects to achieve stable force control. However, these model-based methods require detailed investigations to characterize the system accurately. Deep reinforcement learning (DRL) has proved to be an effective model-free method for continuous control tasks, where few works deal with hardware learning. This paper describes the training process of a DRL policy on the hardware of an SEA pendulum system for tracking force control trajectories from 0.05 - 0.35 Hz at 50 N amplitude using the Proximal Policy Optimization (PPO) algorithm. Safety mechanisms are developed and utilized for training the policy for over 21 hours (including overnight) without an operator present. The tracking performance is evaluated showing improvements of 25 N in mean absolute error when comparing the first 18 minutes of training to the full 21 hours for a 50 N amplitude, 0.1 Hz sinusoid desired force trajectory. Finally, the DRL policy exhibits better tracking and stability margins when compared to a model-free PID controller for a 50 N chirp force trajectory.

In haptic time delayed teleoperation as the time delay from the communication channel increases, teleoperation system stability and performance degrade. To increase performance and provide better stability margins, various estimation methods and observers have been implemented in literature to more accurately capture the force exerted by the remote system. Previously, solutions focused on environment force estimation methods that primarily rely on linearization of the Hunt-Crossley (HC) contact model, which has limiting assumptions for use. This work addresses the shortcomings of the aforementioned methods by investigating alternative HC parameter estimation techniques. A new application of Chebyshev polynomial approximation for adaptive parameter estimation of the HC model is proposed. This approximation is compared to current linearization methods as well as nonlinear estimation methods that are not well covered in literature. Moreover, the Chebyshev approximation is used in a new estimation approach that provides control via backstepping with adaptive parameter estimation using Lyapunov methods. This method reduces excitation requirements by using nonlinear swapping and the data accumulation concept to guarantee parameter convergence. A simulated full teleoperation system with time delay demonstrates the effectiveness of this approach.

We present an approach for achieving a natural, efficient gait on bipedal robots using straightened legs and toe-off. Our algorithm avoids complex height planning by allowing a whole-body controller to determine the straightest possible leg configuration at run-time. The controller solutions are biased towards a straight leg configuration by projecting leg joint angle objectives into the null-space of the other quadratic program motion objectives. To allow the legs to remain straight throughout the gait, toe-off was utilized to increase the kinematic reachability of the legs. The toe-off motion is achieved through underconstraining the foot position, allowing it to emerge naturally. We applied this approach of under-specifying the motion objectives to the Atlas humanoid, allowing it to walk over a variety of terrain. We present both experimental and simulation results and discuss performance limitations and potential improvements.

While humans are highly capable of recovering from external disturbances and uncertainties that result in large tracking errors, humanoid robots have yet to reliably mimic this level of robustness. Essential to this is the ability to combine traditional “ankle strategy” balancing with step timing and location adjustment techniques. In doing so, the robot is able to step quickly to the necessary location to continue walking. In this work, we present both a new swing speed up algorithm to adjust the step timing, allowing the robot to set the foot down more quickly to recover from errors in the direction of the current capture point dynamics, and a new algorithm to adjust the desired footstep, expanding the base of support to utilize the center of pressure (CoP)-based ankle strategy for balance. We then utilize the desired centroidal moment pivot (CMP) to calculate the momentum rate of change for our inverse-dynamics based whole-body controller. We present simulation and experimental results using this work, and discuss performance limitations and potential improvements.

This paper presents an extension of previous model predictive control (MPC) schemes to the stabilization of the time-varying divergent component of motion (DCM). To address the control authority limitations caused by fixed footholds, the step positions and rotations are treated as control inputs, allowing the generation and execution of stable walking motions, both at high speeds and in the face of disturbances. Rotation approximations are handled by applying a mixed-integer program, which, when combined with the use of the time-varying DCM to account for the effects of height changes, improve the versatility of MPC. Simulation results of fast walking and step recovery with the ESCHER humanoid demonstrate the effectiveness of this approach.

To operate in human environments, robots must be able to withstand external disturbances. Small disturbances can be stabilized through momentum regulation, but larger ones require steps to prevent falling. This work presents two new techniques for disturbance rejection. The first is an extension of divergent component of motion (DCM) and capture point tracking controllers that augments a PI feedback control law with a disturbance observer. This is used to estimate transient disturbances through momentum-rate-of-change error. For larger disturbances, we present a novel optimization-based framework based on the DCM dynamics that uses a quadratic program to compute the desired ground reaction forces and recovery step location. Using optimization gives a flexibility that enables planning angular-momentum-rate-of-change trajectories to help reduce recovery step length. We then illustrate the effectiveness of these methods with hardware and simulation experiments of the THOR humanoid.

This paper presents a compliant locomotion framework for torque-controlled humanoids using model-based whole-body control. In order to stabilize the centroidal dynamics during locomotion, we compute linear momentum rate of change objectives using a novel time-varying controller for the Divergent Component of Motion (DCM). Task-space objectives, including the desired momentum rate of change, are tracked using an efficient quadratic program formulation that computes optimal joint torque setpoints given frictional contact constraints and joint position / torque limits. In order to validate the effectiveness of the proposed approach, we demonstrate push recovery and compliant walking using THOR, a 34 DOF humanoid with series elastic actuation. We discuss details leading to the successful implementation of optimization-based whole-body control on our hardware platform, including the design of a “simple” joint impedance controller that introduces inner-loop velocity feedback into the actuator force controller.

This paper provides an overview of the embedded joint-space control approach developed for THOR, a new series elastic humanoid. The 60 kg robot features electromechanical linear series elastic actuators (SEAs), enabling low-impedance control of each joint in the lower body via linear to rotary and parallel mechanisms. We present a distributed joint impedance control framework that leverages a custom dual-axis motor controller to track position, velocity, and torque setpoints for each pair of joints. The required actuator forces are tracked using an inner force control loop combining feedforward and PID control with a model-based disturbance observer (DOB). Unlike previous approaches, we utilize an inverse plant model based on the open-loop actuator dynamics to simplify tuning of the cascaded controller by decoupling DOB estimates from the inner loop gains. The effectiveness of the proposed approach is verified through trajectory tracking and dynamic walking experiments conducted on the THOR humanoid utilizing a complementary optimization-based whole-body controller.

This work presents a method for deriving a gain scheduled balance controller to stabilize the gate of a three legged under-actuated robotic platform called THALeR (Tri-Pedal Hyper Altitudinal Legged Robot). The scheduler adapts the controller gains in real time based upon the system's instantaneous potential energy in order to create a smooth, stable gait. The final controller is simulated with white noise and impulse perturbations to show robustness.

A grid map generated from ground-based 3D LIDAR point clouds is a critical component for facilitating autonomous system navigation without crashing into obstacles and also to generate a road map for a large local area. This paper proposes a novel approach to generate an occupancy grid map along with object classification in real-time for autonomous ground robot applications. Based on geometric analysis of the raster-scanned LIDAR data, we formulate criteria to distinguish 3D points directly from coordinate values and generate three grid maps; occupancy, ground, and scatter maps which directly correspond to hypotheses on object type. Then 2D and 3D shape analysis are carried out to verify the object hypotheses. Our experimental results show that the new method performs well providing an autonomous system with surrounding 3D information and object classification.