720 research outputs found
Walking Stabilization Using Step Timing and Location Adjustment on the Humanoid Robot, Atlas
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
Push recovery with stepping strategy based on time-projection control
In this paper, we present a simple control framework for on-line push
recovery with dynamic stepping properties. Due to relatively heavy legs in our
robot, we need to take swing dynamics into account and thus use a linear model
called 3LP which is composed of three pendulums to simulate swing and torso
dynamics. Based on 3LP equations, we formulate discrete LQR controllers and use
a particular time-projection method to adjust the next footstep location
on-line during the motion continuously. This adjustment, which is found based
on both pelvis and swing foot tracking errors, naturally takes the swing
dynamics into account. Suggested adjustments are added to the Cartesian 3LP
gaits and converted to joint-space trajectories through inverse kinematics.
Fixed and adaptive foot lift strategies also ensure enough ground clearance in
perturbed walking conditions. The proposed structure is robust, yet uses very
simple state estimation and basic position tracking. We rely on the physical
series elastic actuators to absorb impacts while introducing simple laws to
compensate their tracking bias. Extensive experiments demonstrate the
functionality of different control blocks and prove the effectiveness of
time-projection in extreme push recovery scenarios. We also show self-produced
and emergent walking gaits when the robot is subject to continuous dragging
forces. These gaits feature dynamic walking robustness due to relatively soft
springs in the ankles and avoiding any Zero Moment Point (ZMP) control in our
proposed architecture.Comment: 20 pages journal pape
Deep Imitation Learning for Humanoid Loco-manipulation through Human Teleoperation
We tackle the problem of developing humanoid loco-manipulation skills with
deep imitation learning. The difficulty of collecting task demonstrations and
training policies for humanoids with a high degree of freedom presents
substantial challenges. We introduce TRILL, a data-efficient framework for
training humanoid loco-manipulation policies from human demonstrations. In this
framework, we collect human demonstration data through an intuitive Virtual
Reality (VR) interface. We employ the whole-body control formulation to
transform task-space commands by human operators into the robot's joint-torque
actuation while stabilizing its dynamics. By employing high-level action
abstractions tailored for humanoid loco-manipulation, our method can
efficiently learn complex sensorimotor skills. We demonstrate the effectiveness
of TRILL in simulation and on a real-world robot for performing various
loco-manipulation tasks. Videos and additional materials can be found on the
project page: https://ut-austin-rpl.github.io/TRILL.Comment: Submitted to Humanoids 202
Contact-Implicit Trajectory Optimization Based on a Variable Smooth Contact Model and Successive Convexification
In this paper, we propose a contact-implicit trajectory optimization (CITO)
method based on a variable smooth contact model (VSCM) and successive
convexification (SCvx). The VSCM facilitates the convergence of gradient-based
optimization without compromising physical fidelity. On the other hand, the
proposed SCvx-based approach combines the advantages of direct and shooting
methods for CITO. For evaluations, we consider non-prehensile manipulation
tasks. The proposed method is compared to a version based on iterative linear
quadratic regulator (iLQR) on a planar example. The results demonstrate that
both methods can find physically-consistent motions that complete the tasks
without a meaningful initial guess owing to the VSCM. The proposed SCvx-based
method outperforms the iLQR-based method in terms of convergence, computation
time, and the quality of motions found. Finally, the proposed SCvx-based method
is tested on a standard robot platform and shown to perform efficiently for a
real-world application.Comment: Accepted for publication in ICRA 201
Legged Robots for Object Manipulation: A Review
Legged robots can have a unique role in manipulating objects in dynamic,
human-centric, or otherwise inaccessible environments. Although most legged
robotics research to date typically focuses on traversing these challenging
environments, many legged platform demonstrations have also included "moving an
object" as a way of doing tangible work. Legged robots can be designed to
manipulate a particular type of object (e.g., a cardboard box, a soccer ball,
or a larger piece of furniture), by themselves or collaboratively. The
objective of this review is to collect and learn from these examples, to both
organize the work done so far in the community and highlight interesting open
avenues for future work. This review categorizes existing works into four main
manipulation methods: object interactions without grasping, manipulation with
walking legs, dedicated non-locomotive arms, and legged teams. Each method has
different design and autonomy features, which are illustrated by available
examples in the literature. Based on a few simplifying assumptions, we further
provide quantitative comparisons for the range of possible relative sizes of
the manipulated object with respect to the robot. Taken together, these
examples suggest new directions for research in legged robot manipulation, such
as multifunctional limbs, terrain modeling, or learning-based control, to
support a number of new deployments in challenging indoor/outdoor scenarios in
warehouses/construction sites, preserved natural areas, and especially for home
robotics.Comment: Preprint of the paper submitted to Frontiers in Mechanical
Engineerin
Motion Control of the Hybrid Wheeled-Legged Quadruped Robot Centauro
Emerging applications will demand robots to deal with a complex environment, which lacks the structure and predictability of the industrial workspace. Complex scenarios will require robot complexity to increase as well, as compared to classical topologies such as fixed-base manipulators, wheeled mobile platforms, tracked vehicles, and their combinations. Legged robots, such as humanoids and quadrupeds, promise to provide platforms which are flexible enough to handle real world scenarios; however, the improved flexibility comes at the cost of way higher control complexity. As a trade-off, hybrid wheeled-legged robots have been proposed, resulting in the mitigation of control complexity whenever the ground surface is suitable for driving. Following this idea, a new hybrid robot called Centauro has been developed inside the Humanoid and Human Centered Mechatronics lab at Istituto Italiano di Tecnologia (IIT). Centauro is a wheeled-legged quadruped with a humanoid bi-manual upper-body. Differently from other platform of similar concept, Centauro employs customized actuation units, which provide high torque outputs, moderately fast motions, and the possibility to control the exerted torque. Moreover, with more than forty motors moving its limbs, Centauro is a very redundant platform, with the potential to execute many different tasks at the same time. This thesis deals with the design and development of a software architecture, and a control system, tailored to such a robot; both wheeled and legged locomotion strategies have been studied, as well as prioritized, whole-body and interaction controllers exploiting the robot torque control capabilities, and capable to handle the system redundancy. A novel software architecture, made of (i) a real-time robotic middleware, and (ii) a framework for online, prioritized Cartesian controller, forms the basis of the entire work
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