99 research outputs found
Robust and Adaptive Door Operation with a Mobile Robot
The ability to deal with articulated objects is very important for robots
assisting humans. In this work, a framework to robustly and adaptively operate
common doors, using an autonomous mobile manipulator, is proposed. To push
forward the state-of-the-art in robustness and speed performance, we devise a
novel algorithm that fuses a convolutional neural network with efficient point
cloud processing. This advancement enables real-time grasping pose estimation
for multiple handles from RGB-D images, providing a speed up improvement for
assistive human-centered applications. In addition, we propose a versatile
Bayesian framework that endows the robot with the ability to infer the door
kinematic model from observations of its motion and learn from previous
experiences or human demonstrations. Combining these algorithms with a Task
Space Region motion planner, we achieve efficient door operation regardless of
the kinematic model. We validate our framework with real-world experiments
using the Toyota Human Support Robot.Comment: 14 pages, 14 figure
Robust Dynamic Locomotion via Reinforcement Learning and Novel Whole Body Controller
We propose a robust dynamic walking controller consisting of a dynamic
locomotion planner, a reinforcement learning process for robustness, and a
novel whole-body locomotion controller (WBLC). Previous approaches specify
either the position or the timing of steps, however, the proposed locomotion
planner simultaneously computes both of these parameters as locomotion outputs.
Our locomotion strategy relies on devising a reinforcement learning (RL)
approach for robust walking. The learned policy generates multi step walking
patterns, and the process is quick enough to be suitable for real-time
controls. For learning, we devise an RL strategy that uses a phase space
planner (PSP) and a linear inverted pendulum model to make the problem
tractable and very fast. Then, the learned policy is used to provide goal-based
commands to the WBLC, which calculates the torque commands to be executed in
full-humanoid robots. The WBLC combines multiple prioritized tasks and
calculates the associated reaction forces based on practical inequality
constraints. The novel formulation includes efficient calculation of the time
derivatives of various Jacobians. This provides high-fidelity dynamic control
of fast motions. More specifically, we compute the time derivative of the
Jacobian for various tasks and the Jacobian of the centroidal momentum task by
utilizing Lie group operators and operational space dynamics respectively. The
integration of RL-PSP and the WBLC provides highly robust, versatile, and
practical locomotion including steering while walking and handling push
disturbances of up to 520 N during an interval of 0.1 sec. Theoretical and
numerical results are tested through a 3D physics-based simulation of the
humanoid robot Valkyrie.Comment: 15 pages, 12 figure
A Framework for Planning and Controlling Non-Periodic Bipedal Locomotion
This study presents a theoretical framework for planning and controlling
agile bipedal locomotion based on robustly tracking a set of non-periodic apex
states. Based on the prismatic inverted pendulum model, we formulate a hybrid
phase-space planning and control framework which includes the following key
components: (1) a step transition solver that enables dynamically tracking
non-periodic apex or keyframe states over various types of terrains, (2) a
robust hybrid automaton to effectively formulate planning and control
algorithms, (3) a phase-space metric to measure distance to the planned
locomotion manifolds, and (4) a hybrid control method based on the previous
distance metric to produce robust dynamic locomotion under external
disturbances. Compared to other locomotion frameworks, we have a larger focus
on non-periodic gait generation and robustness metrics to deal with
disturbances. Such focus enables the proposed control framework to robustly
track non-periodic apex states over various challenging terrains and under
external disturbances as illustrated through several simulations. Additionally,
it allows a bipedal robot to perform non-periodic bouncing maneuvers over
disjointed terrains.Comment: 33 pages, 18 figures, journa
Full-Body Collision Detection and Reaction with Omnidirectional Mobile Platforms: A Step Towards Safe Human-Robot Interaction
In this paper, we develop estimation and control methods for quickly reacting
to collisions between omnidirectional mobile platforms and their environment.
To enable the full-body detection of external forces, we use torque sensors
located in the robot's drivetrain. Using model based techniques we estimate,
with good precision, the location, direction, and magnitude of collision
forces, and we develop an admittance controller that achieves a low effective
mass in reaction to them. For experimental testing, we use a facility
containing a calibrated collision dummy and our holonomic mobile platform. We
subsequently explore collisions with the dummy colliding against a stationary
base and the base colliding against a stationary dummy. Overall, we accomplish
fast reaction times and a reduction of impact forces. A proof of concept
experiment presents various parts of the mobile platform, including the wheels,
colliding safely with humans.Comment: 17 pages, 11 figures, submitted to Springer's Autonomous Robot
Robust Optimal Planning and Control of Non-Periodic Bipedal Locomotion with A Centroidal Momentum Model
This study presents a theoretical method for planning and controlling agile
bipedal locomotion based on robustly tracking a set of non-periodic keyframe
states. Based on centroidal momentum dynamics, we formulate a hybrid
phase-space planning and control method which includes the following key
components: (i) a step transition solver that enables dynamically tracking
non-periodic keyframe states over various types of terrains, (ii) a robust
hybrid automaton to effectively formulate planning and control algorithms,
(iii) a steering direction model to control the robot's heading, (iv) a
phase-space metric to measure distance to the planned locomotion manifolds, and
(v) a hybrid control method based on the previous distance metric to produce
robust dynamic locomotion under external disturbances. Compared to other
locomotion methodologies, we have a large focus on non-periodic gait generation
and robustness metrics to deal with disturbances. Such focus enables the
proposed control method to robustly track non-periodic keyframe states over
various challenging terrains and under external disturbances as illustrated
through several simulations.Comment: 43 pages, 22 figures, journal, International Journal of Robotics
Research, 2017. arXiv admin note: substantial text overlap with
arXiv:1701.05929, arXiv:1511.0462
Assessing Whole-Body Operational Space Control in a Point-Foot Series Elastic Biped: Balance on Split Terrain and Undirected Walking
In this paper we present advancements in control and trajectory generation
for agile behavior in bipedal robots. We demonstrate that Whole-Body
Operational Space Control (WBOSC), developed a few years ago, is well suited
for achieving two types of agile behaviors, namely, balancing on a high pitch
split terrain and achieving undirected walking on flat terrain. The work
presented here is the first implementation of WBOSC on a biped robot, and more
specifically a biped robot with series elastic actuators. We present and
analyze a new algorithm that dynamically balances point foot robots by choosing
footstep placements. Dealing with the naturally unstable dynamics of these type
of systems is a difficult problem that requires both the controller and the
trajectory generation algorithm to operate quickly and efficiently. We put
forth a comprehensive development and integration effort: the design and
construction of the biped system and experimental infrastructure, a
customization of WBOSC for the agile behaviors, and new trajectory generation
algorithms. Using this custom built controller, we conduct, for first time, an
experiment in which a biped robot balances in a high pitch split terrain,
demonstrating our ability to precisely regulate internal forces using force
sensing feedback techniques. Finally, we demonstrate the stabilizing
capabilities of our online trajectory generation algorithm in the physics-based
simulator and through physical experiments with a planarized locomotion setup.Comment: 17 pages, 9 figures, 4 table
Modeling and Loop Shaping of Single-Joint Amplification Exoskeleton with Contact Sensing and Series Elastic Actuation
In this paper we consider a class of exoskeletons designed to amplify the
strength of humans through feedback of sensed human-robot interactions and
actuator forces. We define an amplification error signal based on a reference
amplification rate, and design a linear feedback compensator to attenuate this
error. Since the human operator is an integral part of the system, we design
the compensator to be robust to both a realistic variation in human impedance
and a large variation in load impedance. We demonstrate our strategy on a
one-degree of freedom amplification exoskeleton connected to a human arm,
following a three dimensional matrix of experimentation: slow or fast human
motion; light or extreme exoskeleton load; and soft or clenched human arm
impedances. We demonstrate that a slightly aggressive controller results in a
borderline stable system---but only for soft human musculoeskeletal behavior
and a heavy load. This class of exoskeleton systems is interesting because it
can both amplify a human's interaction forces --- so long as the human contacts
the environment through the exoskeleton --- and attenuate the operator's
perception of the exoskeleton's reflected dynamics at frequencies within the
bandwidth of the control.Comment: 8 pages, 12 figures, 4 tables. Accepted for publication at the 2019
American Control Conference. Copyright IEEE 201
Social Navigation Planning Based on People's Awareness of Robots
When mobile robots maneuver near people, they run the risk of rudely blocking
their paths; but not all people behave the same around robots. People that have
not noticed the robot are the most difficult to predict. This paper
investigates how mobile robots can generate acceptable paths in dynamic
environments by predicting human behavior. Here, human behavior may include
both physical and mental behavior, we focus on the latter. We introduce a
simple safe interaction model: when a human seems unaware of the robot, it
should avoid going too close. In this study, people around robots are detected
and tracked using sensor fusion and filtering techniques. To handle
uncertainties in the dynamic environment, a Partially-Observable Markov
Decision Process Model (POMDP) is used to formulate a navigation planning
problem in the shared environment. People's awareness of robots is inferred and
included as a state and reward model in the POMDP. The proposed planner enables
a robot to change its navigation plan based on its perception of each person's
robot-awareness. As far as we can tell, this is a new capability. We conduct
simulation and experiments using the Toyota Human Support Robot (HSR) to
validate our approach. We demonstrate that the proposed framework is capable of
running in real-time.Comment: 8pages, 7 figure
Nested Mixture of Experts: Cooperative and Competitive Learning of Hybrid Dynamical System
Model-based reinforcement learning (MBRL) algorithms can attain significant
sample efficiency but require an appropriate network structure to represent
system dynamics. Current approaches include white-box modeling using analytic
parameterizations and black-box modeling using deep neural networks. However,
both can suffer from a bias-variance trade-off in the learning process, and
neither provides a structured method for injecting domain knowledge into the
network. As an alternative, gray-box modeling leverages prior knowledge in
neural network training but only for simple systems. In this paper, we devise a
nested mixture of experts (NMOE) for representing and learning hybrid dynamical
systems. An NMOE combines both white-box and black-box models while optimizing
bias-variance trade-off. Moreover, an NMOE provides a structured method for
incorporating various types of prior knowledge by training the associative
experts cooperatively or competitively. The prior knowledge includes
information on robots' physical contacts with the environments as well as their
kinematic and dynamic properties. In this paper, we demonstrate how to
incorporate prior knowledge into our NMOE in various continuous control
domains, including hybrid dynamical systems. We also show the effectiveness of
our method in terms of data-efficiency, generalization to unseen data, and
bias-variance trade-off. Finally, we evaluate our NMOE using an MBRL setup,
where the model is integrated with a model-based controller and trained online.Comment: Submitted to 2021 L4D
Exploiting the Natural Dynamics of Series Elastic Robots by Actuator-Centered Sequential Linear Programming
Series elastic robots are best able to follow trajectories which obey the
limitations of their actuators, since they cannot instantly change their joint
forces. In fact, the performance of series elastic actuators can surpass that
of ideal force source actuators by storing and releasing energy. In this paper,
we formulate the trajectory optimization problem for series elastic robots in a
novel way based on sequential linear programming. Our framework is unique in
the separation of the actuator dynamics from the rest of the dynamics, and in
the use of a tunable pseudo-mass parameter that improves the discretization
accuracy of our approach. The actuator dynamics are truly linear, which allows
them to be excluded from trust-region mechanics. This causes our algorithm to
have similar run times with and without the actuator dynamics. We demonstrate
our optimization algorithm by tuning high performance behaviors for a
single-leg robot in simulation and on hardware for a single degree-of-freedom
actuator testbed. The results show that compliance allows for faster motions
and takes a similar amount of computation time
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