784 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
Comparing trotting and turning strategies on the quadrupedal Oncilla Robot
In this paper, we compare three different trotting techniques and five different turning strategies on a small, compliant, biologically inspired quadrupedal robot, the Oncilla. The locomotion techniques were optimized on the actual hardware using a treadmill setup, without relying on models. We found that using half ellipses as foot trajectories resulted in the fastest gaits, as well as the highest robustness against parameter changes. Furthermore, we analyzed the importance of using the scapulae for turning, from which we observed that although not necessary, they are needed for turning with a higher speed
Robust Legged Robot State Estimation Using Factor Graph Optimization
Legged robots, specifically quadrupeds, are becoming increasingly attractive
for industrial applications such as inspection. However, to leave the
laboratory and to become useful to an end user requires reliability in harsh
conditions. From the perspective of state estimation, it is essential to be
able to accurately estimate the robot's state despite challenges such as uneven
or slippery terrain, textureless and reflective scenes, as well as dynamic
camera occlusions. We are motivated to reduce the dependency on foot contact
classifications, which fail when slipping, and to reduce position drift during
dynamic motions such as trotting. To this end, we present a factor graph
optimization method for state estimation which tightly fuses and smooths
inertial navigation, leg odometry and visual odometry. The effectiveness of the
approach is demonstrated using the ANYmal quadruped robot navigating in a
realistic outdoor industrial environment. This experiment included trotting,
walking, crossing obstacles and ascending a staircase. The proposed approach
decreased the relative position error by up to 55% and absolute position error
by 76% compared to kinematic-inertial odometry.Comment: 8 pages, 12 figures. Accepted to RA-L + IROS 2019, July 201
Evolved embodied phase coordination enables robust quadruped robot locomotion
Overcoming robotics challenges in the real world requires resilient control
systems capable of handling a multitude of environments and unforeseen events.
Evolutionary optimization using simulations is a promising way to automatically
design such control systems, however, if the disparity between simulation and
the real world becomes too large, the optimization process may result in
dysfunctional real-world behaviors. In this paper, we address this challenge by
considering embodied phase coordination in the evolutionary optimization of a
quadruped robot controller based on central pattern generators. With this
method, leg phases, and indirectly also inter-leg coordination, are influenced
by sensor feedback.By comparing two very similar control systems we gain
insight into how the sensory feedback approach affects the evolved parameters
of the control system, and how the performances differs in simulation, in
transferal to the real world, and to different real-world environments. We show
that evolution enables the design of a control system with embodied phase
coordination which is more complex than previously seen approaches, and that
this system is capable of controlling a real-world multi-jointed quadruped
robot.The approach reduces the performance discrepancy between simulation and
the real world, and displays robustness towards new environments.Comment: 9 page
Robust High-speed Running for Quadruped Robots via Deep Reinforcement Learning
Deep reinforcement learning has emerged as a popular and powerful way to
develop locomotion controllers for quadruped robots. Common approaches have
largely focused on learning actions directly in joint space, or learning to
modify and offset foot positions produced by trajectory generators. Both
approaches typically require careful reward shaping and training for millions
of time steps, and with trajectory generators introduce human bias into the
resulting control policies. In this paper, we instead explore learning foot
positions in Cartesian space, which we track with impedance control, for a task
of running as fast as possible subject to environmental disturbances. Compared
with other action spaces, we observe less needed reward shaping, much improved
sample efficiency, the emergence of natural gaits such as galloping and
bounding, and ease of sim-to-sim transfer. Policies can be learned in only a
few million time steps, even for challenging tasks of running over rough
terrain with loads of over 100% of the nominal quadruped mass. Training occurs
in PyBullet, and we perform a sim-to-sim transfer to Gazebo, where our
quadruped is able to run at over 4 m/s without a load, and 3.5 m/s with a 10 kg
load, which is over 83% of the nominal quadruped mass. Video results can be
found at https://youtu.be/roE1vxpEWfw.Comment: arXiv admin note: text overlap with arXiv:2011.0708
SYNLOCO: Synthesizing Central Pattern Generator and Reinforcement Learning for Quadruped Locomotion
The Central Pattern Generator (CPG) is adept at generating rhythmic gait
patterns characterized by consistent timing and adequate foot clearance. Yet,
its open-loop configuration often compromises the system's control performance
in response to environmental variations. On the other hand, Reinforcement
Learning (RL), celebrated for its model-free properties, has gained significant
traction in robotics due to its inherent adaptability and robustness. However,
initiating traditional RL approaches from the ground up presents computational
challenges and a heightened risk of converging to suboptimal local minima. In
this paper, we propose an innovative quadruped locomotion framework, SYNLOCO,
by synthesizing CPG and RL that can ingeniously integrate the strengths of both
methods, enabling the development of a locomotion controller that is both
stable and natural. Furthermore, we introduce a set of performance-driven
reward metrics that augment the learning of locomotion control. To optimize the
learning trajectory of SYNLOCO, a two-phased training strategy is presented.
Our empirical evaluation, conducted on a Unitree GO1 robot under varied
conditions--including distinct velocities, terrains, and payload
capacities--showcases SYNLOCO's ability to produce consistent and clear-footed
gaits across diverse scenarios. The developed controller exhibits resilience
against substantial parameter variations, underscoring its potential for robust
real-world applications.Comment: 7 Page
- …