201 research outputs found
Fast and Continuous Foothold Adaptation for Dynamic Locomotion through CNNs
Legged robots can outperform wheeled machines for most navigation tasks
across unknown and rough terrains. For such tasks, visual feedback is a
fundamental asset to provide robots with terrain-awareness. However, robust
dynamic locomotion on difficult terrains with real-time performance guarantees
remains a challenge. We present here a real-time, dynamic foothold adaptation
strategy based on visual feedback. Our method adjusts the landing position of
the feet in a fully reactive manner, using only on-board computers and sensors.
The correction is computed and executed continuously along the swing phase
trajectory of each leg. To efficiently adapt the landing position, we implement
a self-supervised foothold classifier based on a Convolutional Neural Network
(CNN). Our method results in an up to 200 times faster computation with respect
to the full-blown heuristics. Our goal is to react to visual stimuli from the
environment, bridging the gap between blind reactive locomotion and purely
vision-based planning strategies. We assess the performance of our method on
the dynamic quadruped robot HyQ, executing static and dynamic gaits (at speeds
up to 0.5 m/s) in both simulated and real scenarios; the benefit of safe
foothold adaptation is clearly demonstrated by the overall robot behavior.Comment: 9 pages, 11 figures. Accepted to RA-L + ICRA 2019, January 201
Learning Image-Conditioned Dynamics Models for Control of Under-actuated Legged Millirobots
Millirobots are a promising robotic platform for many applications due to
their small size and low manufacturing costs. Legged millirobots, in
particular, can provide increased mobility in complex environments and improved
scaling of obstacles. However, controlling these small, highly dynamic, and
underactuated legged systems is difficult. Hand-engineered controllers can
sometimes control these legged millirobots, but they have difficulties with
dynamic maneuvers and complex terrains. We present an approach for controlling
a real-world legged millirobot that is based on learned neural network models.
Using less than 17 minutes of data, our method can learn a predictive model of
the robot's dynamics that can enable effective gaits to be synthesized on the
fly for following user-specified waypoints on a given terrain. Furthermore, by
leveraging expressive, high-capacity neural network models, our approach allows
for these predictions to be directly conditioned on camera images, endowing the
robot with the ability to predict how different terrains might affect its
dynamics. This enables sample-efficient and effective learning for locomotion
of a dynamic legged millirobot on various terrains, including gravel, turf,
carpet, and styrofoam. Experiment videos can be found at
https://sites.google.com/view/imageconddy
A literature review on the optimization of legged robots
Over the last two decades the research and development of legged locomotion robots has grown steadily. Legged
systems present major advantages when compared with ‘traditional’ vehicles, because they allow locomotion in inaccessible
terrain to vehicles with wheels and tracks. However, the robustness of legged robots, and especially their energy
consumption, among other aspects, still lag behind mechanisms that use wheels and tracks. Therefore, in the present
state of development, there are several aspects that need to be improved and optimized. Keeping these ideas in mind,
this paper presents the review of the literature of different methods adopted for the optimization of the structure
and locomotion gaits of walking robots. Among the distinct possible strategies often used for these tasks are referred
approaches such as the mimicking of biological animals, the use of evolutionary schemes to find the optimal parameters
and structures, the adoption of sound mechanical design rules, and the optimization of power-based indexes
Online Optimization-based Gait Adaptation of Quadruped Robot Locomotion
Quadruped robots demonstrated extensive capabilities of traversing complex and unstructured
environments. Optimization-based techniques gave a relevant impulse to the research on legged
locomotion. Indeed, by designing the cost function and the constraints, we can guarantee the
feasibility of a motion and impose high-level locomotion tasks, e.g., tracking of a reference
velocity. This allows one to have a generic planning approach without the need to tailor a
specific motion for each terrain, as in the heuristic case. In this context, Model Predictive
Control (MPC) can compensate for model inaccuracies and external disturbances, thanks to
the high-frequency replanning.
The main objective of this dissertation is to develop a Nonlinear MPC (NMPC)-based
locomotion framework for quadruped robots. The aim is to obtain an algorithm which can
be extended to different robots and gaits; in addition, I sought to remove some assumptions
generally done in the literature, e.g., heuristic reference generator and user-defined gait
sequence.
The starting point of my work is the definition of the Optimal Control Problem to generate
feasible trajectories for the Center of Mass. It is descriptive enough to capture the linear and
angular dynamics of the robot as a whole. A simplified model (Single Rigid Body Dynamics
model) is used for the system dynamics, while a novel cost term maximizes leg mobility
to improve robustness in the presence of nonflat terrain. In addition, to test the approach
on the real robot, I dedicated particular effort to implementing both a heuristic reference
generator and an interface for the controller, and integrating them into the controller framework
developed previously by other team members.
As a second contribution of my work, I extended the locomotion framework to deal with a
trot gait. In particular, I generalized the reference generator to be based on optimization.
Exploiting the Linear Inverted Pendulum model, this new module can deal with the underactuation of the trot when only two legs are in contact with the ground, endowing the NMPC
with physically informed reference trajectories to be tracked. In addition, the reference velocities are used to correct the heuristic footholds, obtaining contact locations coherent with
the motion of the base, even though they are not directly optimized.
The model used by the NMPC receives as input the gait sequence, thus with the last part
of my work I developed an online multi-contact planner and integrated it into the MPC
framework. Using a machine learning approach, the planner computes the best feasible option,
even in complex environments, in a few milliseconds, by ranking online a set of discrete options
for footholds, i.e., which leg to move and where to step. To train the network, I designed
a novel function, evaluated offline, which considers the value of the cost of the NMPC and
robustness/stability metrics for each option.
These methods have been validated with simulations and experiments over the three years. I
tested the NMPC on the Hydraulically actuated Quadruped robot (HyQ) of the IIT’s Dynamic
Legged Systems lab, performing omni-directional motions on flat terrain and stepping on
a pallet (both static and relocated during the motion) with a crawl gait. The trajectory
replanning is performed at high-frequency, and visual information of the terrain is included to
traverse uneven terrain. A Unitree Aliengo quadruped robot is used to execute experiments
with the trot gait. The optimization-based reference generator allows the robot to reach a
fixed goal and recover from external pushes without modifying the structure of the NMPC.
Finally, simulations with the Solo robot are performed to validate the neural network-based
contact planning. The robot successfully traverses complex scenarios, e.g., stepping stones,
with both walk and trot gaits, choosing the footholds online.
The achieved results improved the robustness and the performance of the quadruped locomotion.
High-frequency replanning, dealing with a fixed goal, recovering after a push, and the automatic
selection of footholds could help the robots to accomplish important tasks for the humans,
for example, providing support in a disaster response scenario or inspecting an unknown
environment.
In the future, the contact planning will be transferred to the real hardware. Possible developments foresee the optimization of the gait timings, i.e., stance and swing duration, and a
framework which allows the automatic transition between gaits
Bayesian Optimization with Automatic Prior Selection for Data-Efficient Direct Policy Search
One of the most interesting features of Bayesian optimization for direct
policy search is that it can leverage priors (e.g., from simulation or from
previous tasks) to accelerate learning on a robot. In this paper, we are
interested in situations for which several priors exist but we do not know in
advance which one fits best the current situation. We tackle this problem by
introducing a novel acquisition function, called Most Likely Expected
Improvement (MLEI), that combines the likelihood of the priors and the expected
improvement. We evaluate this new acquisition function on a transfer learning
task for a 5-DOF planar arm and on a possibly damaged, 6-legged robot that has
to learn to walk on flat ground and on stairs, with priors corresponding to
different stairs and different kinds of damages. Our results show that MLEI
effectively identifies and exploits the priors, even when there is no obvious
match between the current situations and the priors.Comment: Accepted at ICRA 2018; 8 pages, 4 figures, 1 algorithm; Video at
https://youtu.be/xo8mUIZTvNE ; Spotlight ICRA presentation
https://youtu.be/iiVaV-U6Kq
Quadruped locomotion reference synthesis wıth central pattern generators tuned by evolutionary algorithms
With the recent advances in sensing, actuating and communication tecnologies and in theory for control and navigation; mobile robotic platforms are seen more promising than ever. This is so for many fields ranging from search and rescue in earthquake sites to military applications. Autonomous or teleoperated land vehicles make a major class of these mobile platforms. Legged robots, with their potential virtues in obstacle avoidance and cross-country capabilities stand out for applications on rugged terrain. In the nature, there are a lot of examples where four-legged anatomy embraces both speed and climbing characteristics. This thesis is on the locomotion reference generation of quadruped robots. Reference generation plays a vital role for the success of the locomotion controller. It involves the timing of the steps and the selection of various spatial parameters. The generated references should be suitable to be followed. They should not be over-demanding to cause the robot fall by loosing its balance. Nature tells that the pattern of the steps, that is, the gait, also changes with the speed of locomotion. A well-planned reference generation algorithm should take gait transitions into account. Central Pattern Generators (CPG) are biologically-inspired tools for legged-robot locomotion reference generation. They represent one of the main stream quadruped robot locomotion synthesis approaches, along with Zero Moment Point (ZMP) based techniques and trial–and–error methods. CPGs stand out with their natural convenience for gait transitions. This is so because of the stable limit cycle behavior inhertent in their structure. However, the parameter selection and tuning of this type of reference generators is difficult. Often, trial–and–error iterations are employed to obtain suitable parameters. The background of complicated dynamics and difficulties in reference generation makes automatic tuning of CPGs an interesting area of research. A natural command for a legged robot is the speed of its locomotion. When considered from kinematics point of view, there is no unique set of walking parameters which yield a given desired speed. However, some of the solutions can be more suitable for a stable walk, whereas others may lead to instability and cause robot to fall. This thesis proposes a quadruped gait tuning method based on evolutionary methods. A velocity command is given as the input to the system. A CPG based reference generation method is employed. 3D full-dynamics locomotion simulations with a 16-degrees-of-freedom (DOF) quadruped robot model are performed to assess the fitness of artificial populations. The fitness is measured by three different cost functions. The first cost function measures the amount of support the simulated quadruped receives from torsional virtual springs and dampers opposing the changes in body orientation, whereas the second one is a measure of energy efficiency in the locomotion. The third cost function is a combination of the firs two. Tuning results with the three cost functions are obtained and compared. Cross-over and mutation mechanisms generate new populations. Simulation results verify the merits of the proposed reference generation and tuning method
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