271 research outputs found
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
In silico case studies of compliant robots: AMARSI deliverable 3.3
In the deliverable 3.2 we presented how the morphological computing ap-
proach can significantly facilitate the control strategy in several scenarios,
e.g. quadruped locomotion, bipedal locomotion and reaching. In particular,
the Kitty experimental platform is an example of the use of morphological
computation to allow quadruped locomotion. In this deliverable we continue
with the simulation studies on the application of the different morphological
computation strategies to control a robotic system
Real2Sim2Real Transfer for Control of Cable-driven Robots via a Differentiable Physics Engine
Tensegrity robots, composed of rigid rods and flexible cables, exhibit high
strength-to-weight ratios and extreme deformations, enabling them to navigate
unstructured terrain and even survive harsh impacts. However, they are hard to
control due to their high dimensionality, complex dynamics, and coupled
architecture. Physics-based simulation is one avenue for developing locomotion
policies that can then be transferred to real robots, but modeling tensegrity
robots is a complex task, so simulations experience a substantial sim2real gap.
To address this issue, this paper describes a Real2Sim2Real strategy for
tensegrity robots. This strategy is based on a differential physics engine that
can be trained given limited data from a real robot (i.e. offline measurements
and one random trajectory) and achieve a high enough accuracy to discover
transferable locomotion policies. Beyond the overall pipeline, key
contributions of this work include computing non-zero gradients at contact
points, a loss function, and a trajectory segmentation technique that avoid
conflicts in gradient evaluation during training. The proposed pipeline is
demonstrated and evaluated on a real 3-bar tensegrity robot.Comment: Submitted to ICRA202
System Design and Locomotion of Superball, an Untethered Tensegrity Robot
The Spherical Underactuated Planetary Exploration Robot ball (SUPERball) is an ongoing project within NASA Ames Research Center's Intelligent Robotics Group and the Dynamic Tensegrity Robotics Lab (DTRL). The current SUPERball is the first full prototype of this tensegrity robot platform, eventually destined for space exploration missions. This work, building on prior published discussions of individual components, presents the fully-constructed robot. Various design improvements are discussed, as well as testing results of the sensors and actuators that illustrate system performance. Basic low-level motor position controls are implemented and validated against sensor data, which show SUPERball to be uniquely suited for highly dynamic state trajectory tracking. Finally, SUPERball is shown in a simple example of locomotion. This implementation of a basic motion primitive shows SUPERball in untethered control
Design and control of a collision-resilient aerial vehicle with an icosahedron tensegrity structure
We present the tensegrity aerial vehicle, a design of collision-resilient
rotor robots with icosahedron tensegrity structures. The tensegrity aerial
vehicles can withstand high-speed impacts and resume operation after
collisions. To guide the design process of these aerial vehicles, we propose a
model-based methodology that predicts the stresses in the structure with a
dynamics simulation and selects components that can withstand the predicted
stresses. Meanwhile, an autonomous re-orientation controller is created to help
the tensegrity aerial vehicles resume flight after collisions. The
re-orientation controller can rotate the vehicles from arbitrary orientations
on the ground to ones easy for takeoff. With collision resilience and
re-orientation ability, the tensegrity aerial vehicles can operate in cluttered
environments without complex collision-avoidance strategies. Moreover, by
adopting an inertial navigation strategy of replacing flight with short hops to
mitigate the growth of state estimation error, the tensegrity aerial vehicles
can conduct short-range operations without external sensors. These capabilities
are validated by a test of an experimental tensegrity aerial vehicle operating
with only onboard inertial sensors in a previously-unknown forest.Comment: 12 pages, 16 figure
Multimodal Learning of Soft Robot Dynamics using Differentiable Filters
Differentiable Filters, as recursive Bayesian estimators, possess the ability
to learn complex dynamics by deriving state transition and measurement models
exclusively from data. This data-driven approach eliminates the reliance on
explicit analytical models while maintaining the essential algorithmic
components of the filtering process. However, the gain mechanism remains
non-differentiable, limiting its adaptability to specific task requirements and
contextual variations. To address this limitation, this paper introduces an
innovative approach called {\alpha}-MDF (Attention-based Multimodal
Differentiable Filter). {\alpha}-MDF leverages modern attention mechanisms to
learn multimodal latent representations for accurate state estimation in soft
robots. By incorporating attention mechanisms, {\alpha}-MDF offers the
flexibility to tailor the gain mechanism to the unique nature of the task and
context. The effectiveness of {\alpha}-MDF is validated through real-world
state estimation tasks on soft robots. Our experimental results demonstrate
significant reductions in state estimation errors, consistently surpassing
differentiable filter baselines by up to 45% in the domain of soft robotics.Comment: 13 pages, 8 figures, 5 tables, CoRL 2023 workshop Learning for Soft
Robot
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