1,561 research outputs found
Learning Whole-body Motor Skills for Humanoids
This paper presents a hierarchical framework for Deep Reinforcement Learning
that acquires motor skills for a variety of push recovery and balancing
behaviors, i.e., ankle, hip, foot tilting, and stepping strategies. The policy
is trained in a physics simulator with realistic setting of robot model and
low-level impedance control that are easy to transfer the learned skills to
real robots. The advantage over traditional methods is the integration of
high-level planner and feedback control all in one single coherent policy
network, which is generic for learning versatile balancing and recovery motions
against unknown perturbations at arbitrary locations (e.g., legs, torso).
Furthermore, the proposed framework allows the policy to be learned quickly by
many state-of-the-art learning algorithms. By comparing our learned results to
studies of preprogrammed, special-purpose controllers in the literature,
self-learned skills are comparable in terms of disturbance rejection but with
additional advantages of producing a wide range of adaptive, versatile and
robust behaviors.Comment: 2018 IEEE-RAS 18th International Conference on Humanoid Robots
(Humanoids
Learning Task Priorities from Demonstrations
Bimanual operations in humanoids offer the possibility to carry out more than
one manipulation task at the same time, which in turn introduces the problem of
task prioritization. We address this problem from a learning from demonstration
perspective, by extending the Task-Parameterized Gaussian Mixture Model
(TP-GMM) to Jacobian and null space structures. The proposed approach is tested
on bimanual skills but can be applied in any scenario where the prioritization
between potentially conflicting tasks needs to be learned. We evaluate the
proposed framework in: two different tasks with humanoids requiring the
learning of priorities and a loco-manipulation scenario, showing that the
approach can be exploited to learn the prioritization of multiple tasks in
parallel.Comment: Accepted for publication at the IEEE Transactions on Robotic
HumanMimic: Learning Natural Locomotion and Transitions for Humanoid Robot via Wasserstein Adversarial Imitation
Transferring human motion skills to humanoid robots remains a significant
challenge. In this study, we introduce a Wasserstein adversarial imitation
learning system, allowing humanoid robots to replicate natural whole-body
locomotion patterns and execute seamless transitions by mimicking human
motions. First, we present a unified primitive-skeleton motion retargeting to
mitigate morphological differences between arbitrary human demonstrators and
humanoid robots. An adversarial critic component is integrated with
Reinforcement Learning (RL) to guide the control policy to produce behaviors
aligned with the data distribution of mixed reference motions. Additionally, we
employ a specific Integral Probabilistic Metric (IPM), namely the Wasserstein-1
distance with a novel soft boundary constraint to stabilize the training
process and prevent model collapse. Our system is evaluated on a full-sized
humanoid JAXON in the simulator. The resulting control policy demonstrates a
wide range of locomotion patterns, including standing, push-recovery, squat
walking, human-like straight-leg walking, and dynamic running. Notably, even in
the absence of transition motions in the demonstration dataset, robots showcase
an emerging ability to transit naturally between distinct locomotion patterns
as desired speed changes
Learning a Unified Control Policy for Safe Falling
Being able to fall safely is a necessary motor skill for humanoids performing
highly dynamic tasks, such as running and jumping. We propose a new method to
learn a policy that minimizes the maximal impulse during the fall. The
optimization solves for both a discrete contact planning problem and a
continuous optimal control problem. Once trained, the policy can compute the
optimal next contacting body part (e.g. left foot, right foot, or hands),
contact location and timing, and the required joint actuation. We represent the
policy as a mixture of actor-critic neural network, which consists of n control
policies and the corresponding value functions. Each pair of actor-critic is
associated with one of the n possible contacting body parts. During execution,
the policy corresponding to the highest value function will be executed while
the associated body part will be the next contact with the ground. With this
mixture of actor-critic architecture, the discrete contact sequence planning is
solved through the selection of the best critics while the continuous control
problem is solved by the optimization of actors. We show that our policy can
achieve comparable, sometimes even higher, rewards than a recursive search of
the action space using dynamic programming, while enjoying 50 to 400 times of
speed gain during online execution
Teaching humanoid robotics by means of human teleoperation through RGB-D sensors
This paper presents a graduate course project on humanoid robotics offered by the University of Padova. The target is to safely lift an object by teleoperating a small humanoid. Students have to map human limbs into robot joints, guarantee the robot stability during the motion, and teleoperate the robot to perform the correct movement. We introduce the following innovative aspects with respect to classical robotic classes: i) the use of humanoid robots as teaching tools; ii) the simplification of the stable locomotion problem by exploiting the potential of teleoperation; iii) the adoption of a Project-Based Learning constructivist approach as teaching methodology. The learning objectives of both course and project are introduced and compared with the students\u2019 background. Design and constraints students have to deal with are reported, together with the amount of time they and their instructors dedicated to solve tasks. A set of evaluation results are provided in order to validate the authors\u2019 purpose, including the students\u2019 personal feedback. A discussion about possible future improvements is reported, hoping to encourage further spread of educational robotics in schools at all levels
Robot Composite Learning and the Nunchaku Flipping Challenge
Advanced motor skills are essential for robots to physically coexist with
humans. Much research on robot dynamics and control has achieved success on
hyper robot motor capabilities, but mostly through heavily case-specific
engineering. Meanwhile, in terms of robot acquiring skills in a ubiquitous
manner, robot learning from human demonstration (LfD) has achieved great
progress, but still has limitations handling dynamic skills and compound
actions. In this paper, we present a composite learning scheme which goes
beyond LfD and integrates robot learning from human definition, demonstration,
and evaluation. The method tackles advanced motor skills that require dynamic
time-critical maneuver, complex contact control, and handling partly soft
partly rigid objects. We also introduce the "nunchaku flipping challenge", an
extreme test that puts hard requirements to all these three aspects. Continued
from our previous presentations, this paper introduces the latest update of the
composite learning scheme and the physical success of the nunchaku flipping
challenge
A Developmental Neuro-Robotics Approach for Boosting the Recognition of Handwritten Digits
Developmental psychology and neuroimaging
research identified a close link between numbers and fingers,
which can boost the initial number knowledge in children. Recent
evidence shows that a simulation of the children's embodied
strategies can improve the machine intelligence too. This article
explores the application of embodied strategies to convolutional
neural network models in the context of developmental neurorobotics, where the training information is likely to be gradually
acquired while operating rather than being abundant and fully
available as the classical machine learning scenarios. The
experimental analyses show that the proprioceptive information
from the robot fingers can improve network accuracy in the
recognition of handwritten Arabic digits when training examples
and epochs are few. This result is comparable to brain imaging
and longitudinal studies with young children. In conclusion, these
findings also support the relevance of the embodiment in the case
of artificial agents’ training and show a possible way for the
humanization of the learning process, where the robotic body can
express the internal processes of artificial intelligence making it
more understandable for humans
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