688 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
RLOC: Terrain-Aware Legged Locomotion using Reinforcement Learning and Optimal Control
We present a unified model-based and data-driven approach for quadrupedal
planning and control to achieve dynamic locomotion over uneven terrain. We
utilize on-board proprioceptive and exteroceptive feedback to map sensory
information and desired base velocity commands into footstep plans using a
reinforcement learning (RL) policy trained in simulation over a wide range of
procedurally generated terrains. When ran online, the system tracks the
generated footstep plans using a model-based controller. We evaluate the
robustness of our method over a wide variety of complex terrains. It exhibits
behaviors which prioritize stability over aggressive locomotion. Additionally,
we introduce two ancillary RL policies for corrective whole-body motion
tracking and recovery control. These policies account for changes in physical
parameters and external perturbations. We train and evaluate our framework on a
complex quadrupedal system, ANYmal version B, and demonstrate transferability
to a larger and heavier robot, ANYmal C, without requiring retraining.Comment: 19 pages, 15 figures, 6 tables, 1 algorithm, submitted to T-RO; under
revie
Deep Reinforcement Learning for Tensegrity Robot Locomotion
Tensegrity robots, composed of rigid rods connected by elastic cables, have a
number of unique properties that make them appealing for use as planetary
exploration rovers. However, control of tensegrity robots remains a difficult
problem due to their unusual structures and complex dynamics. In this work, we
show how locomotion gaits can be learned automatically using a novel extension
of mirror descent guided policy search (MDGPS) applied to periodic locomotion
movements, and we demonstrate the effectiveness of our approach on tensegrity
robot locomotion. We evaluate our method with real-world and simulated
experiments on the SUPERball tensegrity robot, showing that the learned
policies generalize to changes in system parameters, unreliable sensor
measurements, and variation in environmental conditions, including varied
terrains and a range of different gravities. Our experiments demonstrate that
our method not only learns fast, power-efficient feedback policies for rolling
gaits, but that these policies can succeed with only the limited onboard
sensing provided by SUPERball's accelerometers. We compare the learned feedback
policies to learned open-loop policies and hand-engineered controllers, and
demonstrate that the learned policy enables the first continuous, reliable
locomotion gait for the real SUPERball robot. Our code and other supplementary
materials are available from http://rll.berkeley.edu/drl_tensegrityComment: International Conference on Robotics and Automation (ICRA), 2017.
Project website link is http://rll.berkeley.edu/drl_tensegrit
Simplifying robotic locomotion by escaping traps via an active tail
Legged systems offer the ability to negotiate and climb heterogeneous terrains, more so than their wheeled counterparts \cite{freedberg_2012}. However, in certain complex environments, these systems are susceptible to failure conditions. These scenarios are caused by the interplay between the locomotor's kinematic state and the local terrain configuration, thus making them challenging to predict and overcome. These failures can cause catastrophic damage to the system and thus, methods to avoid such scenarios have been developed. These strategies typically take the form of environmental sensing or passive mechanical elements that adapt to the terrain. Such methods come at an increased control and mechanical design complexity for the system, often still being susceptible to imperceptible hazards. In this study, we investigated whether a tail could serve to offload this complexity by acting as a mechanism to generate new terradynamic interactions and mitigate failure via substrate contact. To do so, we developed a quadrupedal C-leg robophysical model (length and width = 27 cm, limb radius = 8 cm) capable of walking over rough terrain with an attachable actuated tail (length = 17 cm). We investigated three distinct tail strategies: static pose, periodic tapping, and load-triggered (power) tapping, while varying the angle of the tail relative to the body. We challenged the system to traverse a terrain (length = 160 cm, width = 80 cm) of randomized blocks (length and width = 10 cm, height = 0 to 12 cm) whose dimensions were scaled to the robot. Over this terrain, the robot exhibited trapping failures independent of gait pattern. Using the tail, the robot could free itself from trapping with a probability of 0 to 0.5, with the load-driven behaviors having comparable performance to low frequency periodic tapping across all tested tail angles. Along with increasing this likelihood of freeing, the robot displayed a longer survival distance over the rough terrain with these tail behaviors. In summary, we present the beginning of a framework that leverages mechanics via tail-ground interactions to offload limb control and design complexity to mitigate failure and improve legged system performance in heterogeneous environments.M.S
Review of Quadruped Robots for Dynamic Locomotion
This review introduces quadruped robots: MITCheetah, HyQ, ANYmal, BigDog, and
their mechanical structure, actuation, and control
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