516 research outputs found
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
Body Lift and Drag for a Legged Millirobot in Compliant Beam Environment
Much current study of legged locomotion has rightly focused on foot traction
forces, including on granular media. Future legged millirobots will need to go
through terrain, such as brush or other vegetation, where the body contact
forces significantly affect locomotion. In this work, a (previously developed)
low-cost 6-axis force/torque sensing shell is used to measure the interaction
forces between a hexapedal millirobot and a set of compliant beams, which act
as a surrogate for a densely cluttered environment. Experiments with a
VelociRoACH robotic platform are used to measure lift and drag forces on the
tactile shell, where negative lift forces can increase traction, even while
drag forces increase. The drag energy and specific resistance required to pass
through dense terrains can be measured. Furthermore, some contact between the
robot and the compliant beams can lower specific resistance of locomotion. For
small, light-weight legged robots in the beam environment, the body motion
depends on both leg-ground and body-beam forces. A shell-shape which reduces
drag but increases negative lift, such as the half-ellipsoid used, is suggested
to be advantageous for robot locomotion in this type of environment.Comment: First three authors contributed equally. Accepted to ICRA 201
RL + Model-based Control: Using On-demand Optimal Control to Learn Versatile Legged Locomotion
This letter presents a versatile control method for dynamic and robust legged
locomotion that integrates model-based optimal control with reinforcement
learning (RL). Our approach involves training an RL policy to imitate reference
motions generated on-demand through solving a finite-horizon optimal control
problem. This integration enables the policy to leverage human expertise in
generating motions to imitate while also allowing it to generalize to more
complex scenarios that require a more complex dynamics model. Our method
successfully learns control policies capable of generating diverse quadrupedal
gait patterns and maintaining stability against unexpected external
perturbations in both simulation and hardware experiments. Furthermore, we
demonstrate the adaptability of our method to more complex locomotion tasks on
uneven terrain without the need for excessive reward shaping or hyperparameter
tuning.Comment: 8 pages. 8 figures. The supplementary video is available in
https://youtu.be/gXDP87yVq4
Keep Rollin' - Whole-Body Motion Control and Planning for Wheeled Quadrupedal Robots
We show dynamic locomotion strategies for wheeled quadrupedal robots, which
combine the advantages of both walking and driving. The developed optimization
framework tightly integrates the additional degrees of freedom introduced by
the wheels. Our approach relies on a zero-moment point based motion
optimization which continuously updates reference trajectories. The reference
motions are tracked by a hierarchical whole-body controller which computes
optimal generalized accelerations and contact forces by solving a sequence of
prioritized tasks including the nonholonomic rolling constraints. Our approach
has been tested on ANYmal, a quadrupedal robot that is fully torque-controlled
including the non-steerable wheels attached to its legs. We conducted
experiments on flat and inclined terrains as well as over steps, whereby we
show that integrating the wheels into the motion control and planning framework
results in intuitive motion trajectories, which enable more robust and dynamic
locomotion compared to other wheeled-legged robots. Moreover, with a speed of 4
m/s and a reduction of the cost of transport by 83 % we prove the superiority
of wheeled-legged robots compared to their legged counterparts.Comment: IEEE Robotics and Automation Letter
Understanding the agility of running birds: Sensorimotor and mechanical factors in avian bipedal locomotion
Birds are a diverse and agile lineage of vertebrates that all use bipedal locomotion for at least part of their life. Thus birds provide a valuable opportunity to investigate how biomechanics and sensorimotor control are integrated for agile bipedal locomotion. This review summarizes recent work using terrain perturbations to reveal neuromechanical control strategies used by ground birds to achieve robust, stable and agile running. Early experiments in running guinea fowl aimed to reveal the immediate intrinsic mechanical response to an unexpected drop ('pothole') in terrain. When navigating the pothole, guinea fowl experience large changes in leg posture in the perturbed step, which correlates strongly with leg loading and perturbation recovery. Analysis of simple theoretical models of running has further confirmed the crucial role of swing-leg trajectory control for regulating foot contact timing and leg loading in uneven terrain. Coupling between body and leg dynamics results in an inherent trade-off in swing leg retraction rate for fall avoidance versus injury avoidance. Fast leg retraction minimizes injury risk, but slow leg retraction minimizes fall risk. Subsequent experiments have investigated how birds optimize their control strategies depending on the type of perturbation (pothole, step, obstacle), visibility of terrain, and with ample practice negotiating terrain features. Birds use several control strategies consistently across terrain contexts: 1) independent control of leg angular cycling and leg length actuation, which facilitates dynamic stability through simple control mechanisms, 2) feedforward regulation of leg cycling rate, which tunes foot-contact timing to maintain consistent leg loading in uneven terrain (minimizing fall and injury risks), 3) load-dependent muscle actuation, which rapidly adjusts stance push-off and stabilizes body mechanical energy, and 4) multi-step recovery strategies that allow body dynamics to transiently vary while tightly regulating leg loading to minimize risks of fall and injury. In future work, it will be interesting to investigate the learning and adaptation processes that allow animals to adjust neuromechanical control mechanisms over short and long timescales
Learning Multimodal Bipedal Locomotion and Implicit Transitions: A Versatile Policy Approach
In this paper, we propose a novel framework for synthesizing a single
multimodal control policy capable of generating diverse behaviors (or modes)
and emergent inherent transition maneuvers for bipedal locomotion. In our
method, we first learn efficient latent encodings for each behavior by training
an autoencoder from a dataset of rough reference motions. These latent
encodings are used as commands to train a multimodal policy through an adaptive
sampling of modes and transitions to ensure consistent performance across
different behaviors. We validate the policy performance in simulation for
various distinct locomotion modes such as walking, leaping, jumping on a block,
standing idle, and all possible combinations of inter-mode transitions.
Finally, we integrate a task-based planner to rapidly generate open-loop mode
plans for the trained multimodal policy to solve high-level tasks like reaching
a goal position on a challenging terrain. Complex parkour-like motions by
smoothly combining the discrete locomotion modes were generated in 3 min. to
traverse tracks with a gap of width 0.45 m, a plateau of height 0.2 m, and a
block of height 0.4 m, which are all significant compared to the dimensions of
our mini-biped platform.Comment: 8 pages, 8 figure
Scaling of avian bipedal locomotion reveals independent effects of body mass and leg posture on gait
This work was supported by a grant (‘Mechanics and energetics of stable bipedal locomotion in uneven terrain: Does a trade-off exist between economy and stability?’, BB/H005838/1) to M.A.D. from the Biotechnology and Biological Sciences Research Council (BBSRC)
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