524 research outputs found
Fast Sensing and Adaptive Actuation for Robust Legged Locomotion
Robust legged locomotion in complex terrain demands fast perturbation detection and reaction. In animals, due to the neural transmission delays, the high-level control loop involving the brain is absent from mitigating the initial disturbance. Instead, the low-level compliant behavior embedded in mechanics and the mid-level controllers in the spinal cord are believed to provide quick response during fast locomotion. Still, it remains unclear how these low- and mid-level components facilitate robust locomotion.
This thesis aims to identify and characterize the underlining elements responsible for fast sensing and actuation. To test individual elements and their interplay, several robotic systems were implemented. The implementations include active and passive mechanisms as a combination of elasticities and dampers in multi-segment robot legs, central pattern generators inspired by intraspinal controllers, and a synthetic robotic version of an intraspinal sensor.
The first contribution establishes the notion of effective damping. Effective damping is defined as the total energy dissipation during one step, which allows quantifying how much ground perturbation is mitigated. Using this framework, the optimal damper is identified as viscous and tunable. This study paves the way for integrating effective dampers to legged designs for robust locomotion.
The second contribution introduces a novel series elastic actuation system. The proposed system tackles the issue of power transmission over multiple joints, while featuring intrinsic series elasticity. The design is tested on a hopper with two more elastic elements, demonstrating energy recuperation and enhanced dynamic performance.
The third contribution proposes a novel tunable damper and reveals its influence on legged hopping. A bio-inspired slack tendon mechanism is implemented in parallel with a spring. The tunable damping is rigorously quantified on a central-pattern-generator-driven hopping robot, which reveals the trade-off between locomotion robustness and efficiency.
The last contribution explores the intraspinal sensing hypothesis of birds. We speculate that the observed intraspinal structure functions as an accelerometer. This accelerometer could provide fast state feedback directly to the adjacent central pattern generator circuits, contributing to birds’ running robustness. A biophysical simulation framework is established, which provides new perspectives on the sensing mechanics of the system, including the influence of morphologies and material properties.
Giving an overview of the hierarchical control architecture, this thesis investigates the fast sensing and actuation mechanisms in several control layers, including the low-level mechanical response and the mid-level intraspinal controllers. The contributions of this work provide new insight into animal loco-motion robustness and lays the foundation for future legged robot design
Technical Report on: Tripedal Dynamic Gaits for a Quadruped Robot
A vast number of applications for legged robots entail tasks in complex,
dynamic environments. But these environments put legged robots at high risk for
limb damage. This paper presents an empirical study of fault tolerant dynamic
gaits designed for a quadrupedal robot suffering from a single, known
``missing'' limb. Preliminary data suggests that the featured gait controller
successfully anchors a previously developed planar monopedal hopping template
in the three-legged spatial machine. This compositional approach offers a
useful and generalizable guide to the development of a wider range of tripedal
recovery gaits for damaged quadrupedal machines.Comment: Updated *increased font size on figures 2-6 *added a legend, replaced
text with colors in figure 5a and 6a *made variables representing vectors
boldface in equations 8-10 *expanded on calculations in equations 8-10 by
adding additional lines *added a missing "2" to equation 8 (typo) *added mass
of the robot to tables II and III *increased the width of figures 1 and
Simulation and Control of Running Models
This work focuses on the locomotion of one-legged robots, with focus on approaches that stabilize passive limit cycles. Locomotion based on the socalled passive gaits promises to greatly reduce the actuation effort required for legged robots to move. In this work, the passive gaits of robots of varying complexity are characterized and stabilizing controllers are reviewed from the literature and newly formulated. The robots are modelled as hybrid dynamical systems and numerically simulated, thereby allowing to validate the proposed control strategies.
Firstly, the vertical control through energy regulation of a one-dimensional hopper is considered.
Secondly, the SLIP model is reviewed and then extended to the “pitchingSLIP”, with the aim of characterizing its passive gaits with somersaults. Two controllers based on energy and angular momentum regulation are then formulated to stabilize passive gaits with somersaults, making the control effort converge to zero. A further extension of the SLIP template, denominated “bodySLIP”, is then used to test the control approach on a more realistic model. The controllers shall be later extended to more complex cases, in which the somersaults are not necessarily present in the passive gaits.
Thirdly, the locomotion of a one-legged robot with a body link is studied.
Raibert’s control approach based on the foot placement algorithm is reviewed and compared to the non-dissipative touchdown controller of Hyon and Emura.
The latter is then extended to be used with continuous torque profiles and to perform velocity tracking. Moreover, damping is added to the joints in order to study its effect on the controller, which was then modified to achieve stable running even in such conditions. The results obtained shall lay the foundations for a later test on hardware on DLR’s quadruped Bert
Torque-based Deep Reinforcement Learning for Task-and-Robot Agnostic Learning on Bipedal Robots Using Sim-to-Real Transfer
In this paper, we review the question of which action space is best suited
for controlling a real biped robot in combination with Sim2Real training.
Position control has been popular as it has been shown to be more sample
efficient and intuitive to combine with other planning algorithms. However, for
position control gain tuning is required to achieve the best possible policy
performance. We show that instead, using a torque-based action space enables
task-and-robot agnostic learning with less parameter tuning and mitigates the
sim-to-reality gap by taking advantage of torque control's inherent compliance.
Also, we accelerate the torque-based-policy training process by pre-training
the policy to remain upright by compensating for gravity. The paper showcases
the first successful sim-to-real transfer of a torque-based deep reinforcement
learning policy on a real human-sized biped robot. The video is available at
https://youtu.be/CR6pTS39VRE
Adaptive Tracking of a Single-Rigid-Body Character in Various Environments
Since the introduction of DeepMimic [Peng et al. 2018], subsequent research
has focused on expanding the repertoire of simulated motions across various
scenarios. In this study, we propose an alternative approach for this goal, a
deep reinforcement learning method based on the simulation of a
single-rigid-body character. Using the centroidal dynamics model (CDM) to
express the full-body character as a single rigid body (SRB) and training a
policy to track a reference motion, we can obtain a policy that is capable of
adapting to various unobserved environmental changes and controller transitions
without requiring any additional learning. Due to the reduced dimension of
state and action space, the learning process is sample-efficient. The final
full-body motion is kinematically generated in a physically plausible way,
based on the state of the simulated SRB character. The SRB simulation is
formulated as a quadratic programming (QP) problem, and the policy outputs an
action that allows the SRB character to follow the reference motion. We
demonstrate that our policy, efficiently trained within 30 minutes on an
ultraportable laptop, has the ability to cope with environments that have not
been experienced during learning, such as running on uneven terrain or pushing
a box, and transitions between learned policies, without any additional
learning
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
An online learning algorithm for adapting leg stiffness and stride angle for efficient quadruped robot trotting
Animals adjust their leg stiffness and stride angle in response to changing ground conditions and gait parameters, resulting in improved stability and reduced energy consumption. This paper presents an online learning algorithm that attempts to mimic such animal behavior by maximizing energy efficiency on the fly or equivalently, minimizing the cost of transport of legged robots by adaptively changing the leg stiffness and stride angle while the robot is traversing on grounds with unknown characteristics. The algorithm employs an approximate stochastic gradient method to change the parameters in real-time, and has the following advantages: (1) the algorithm is computationally efficient and suitable for real-time operation; (2) it does not require training; (3) it is model-free, implying that precise modeling of the robot is not required for good performance; and (4) the algorithm is generally applicable and can be easily incorporated into a variety of legged robots with adaptable parameters and gaits beyond those implemented in this paper. Results of exhaustive performance assessment through numerical simulations and experiments on an under-actuated quadruped robot with compliant legs are included in the paper. The robot platform used a pneumatic piston in each leg as a variable, passive compliant element. Performance evaluation using simulations and experiments indicated that the algorithm was capable of converging to near-optimal values of the cost of transport for given operating conditions, terrain properties, and gait characteristics with no prior knowledge of the terrain and gait conditions. The simplicity of the algorithm and its demonstrably improved performance make the approach of this paper an excellent candidate for adaptively controlling tunable parameters of compliant, legged robots
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