10,013 research outputs found
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
Walking Humanoids for Robotics Research
We present three humanoid robots aimed as platforms for research in robotics, and cognitive development in robotics systems. The 'priscilla' robot is a 180cm full scale humanoid, and the mid-size prototype is called 'elvis' and is about 70cm tall. The smallest size humanoid is the 'elvina' type, about 28 cm tall. Two instances of 'elvina' have been built to enable experiments with cooperating humanoids. The underlying ideas and conceptual principles, such as anthropomorphism, embodiment, and mechanisms for learning and adaptivity are introduced as well
MPC-based humanoid pursuit-evasion in the presence of obstacles
We consider a pursuit-evasion problem between humanoids in the presence of obstacles. In our scenario, the pursuer enters the safety area of the evader headed for collision, while the latter executes a fast evasive motion. Control schemes are designed for both the pursuer and the evader. They are structurally identical, although the objectives are different: the pursuer tries to align its direction of motion with the line- of-sight to the evader, whereas the evader tries to move in a direction orthogonal to the line-of-sight to the pursuer. At the core of the control architecture is a Model Predictive Control scheme for generating a stable gait. This allows for the inclusion of workspace obstacles, which we take into account at two levels: during the determination of the footsteps orientation and as an explicit MPC constraint. We illustrate the results with simulations on NAO humanoids
Efficient Humanoid Contact Planning using Learned Centroidal Dynamics Prediction
Humanoid robots dynamically navigate an environment by interacting with it
via contact wrenches exerted at intermittent contact poses. Therefore, it is
important to consider dynamics when planning a contact sequence. Traditional
contact planning approaches assume a quasi-static balance criterion to reduce
the computational challenges of selecting a contact sequence over a rough
terrain. This however limits the applicability of the approach when dynamic
motions are required, such as when walking down a steep slope or crossing a
wide gap. Recent methods overcome this limitation with the help of efficient
mixed integer convex programming solvers capable of synthesizing dynamic
contact sequences. Nevertheless, its exponential-time complexity limits its
applicability to short time horizon contact sequences within small
environments. In this paper, we go beyond current approaches by learning a
prediction of the dynamic evolution of the robot centroidal momenta, which can
then be used for quickly generating dynamically robust contact sequences for
robots with arms and legs using a search-based contact planner. We demonstrate
the efficiency and quality of the results of the proposed approach in a set of
dynamically challenging scenarios
On Time Optimization of Centroidal Momentum Dynamics
Recently, the centroidal momentum dynamics has received substantial attention
to plan dynamically consistent motions for robots with arms and legs in
multi-contact scenarios. However, it is also non convex which renders any
optimization approach difficult and timing is usually kept fixed in most
trajectory optimization techniques to not introduce additional non convexities
to the problem. But this can limit the versatility of the algorithms. In our
previous work, we proposed a convex relaxation of the problem that allowed to
efficiently compute momentum trajectories and contact forces. However, our
approach could not minimize a desired angular momentum objective which
seriously limited its applicability. Noticing that the non-convexity introduced
by the time variables is of similar nature as the centroidal dynamics one, we
propose two convex relaxations to the problem based on trust regions and soft
constraints. The resulting approaches can compute time-optimized dynamically
consistent trajectories sufficiently fast to make the approach realtime
capable. The performance of the algorithm is demonstrated in several
multi-contact scenarios for a humanoid robot. In particular, we show that the
proposed convex relaxation of the original problem finds solutions that are
consistent with the original non-convex problem and illustrate how timing
optimization allows to find motion plans that would be difficult to plan with
fixed timing.Comment: 7 pages, 4 figures, ICRA 201
Balancing experiments on a torque-controlled humanoid with hierarchical inverse dynamics
Recently several hierarchical inverse dynamics controllers based on cascades
of quadratic programs have been proposed for application on torque controlled
robots. They have important theoretical benefits but have never been
implemented on a torque controlled robot where model inaccuracies and real-time
computation requirements can be problematic. In this contribution we present an
experimental evaluation of these algorithms in the context of balance control
for a humanoid robot. The presented experiments demonstrate the applicability
of the approach under real robot conditions (i.e. model uncertainty, estimation
errors, etc). We propose a simplification of the optimization problem that
allows us to decrease computation time enough to implement it in a fast torque
control loop. We implement a momentum-based balance controller which shows
robust performance in face of unknown disturbances, even when the robot is
standing on only one foot. In a second experiment, a tracking task is evaluated
to demonstrate the performance of the controller with more complicated
hierarchies. Our results show that hierarchical inverse dynamics controllers
can be used for feedback control of humanoid robots and that momentum-based
balance control can be efficiently implemented on a real robot.Comment: appears in IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS), 201
Analyzing Whole-Body Pose Transitions in Multi-Contact Motions
When executing whole-body motions, humans are able to use a large variety of
support poses which not only utilize the feet, but also hands, knees and elbows
to enhance stability. While there are many works analyzing the transitions
involved in walking, very few works analyze human motion where more complex
supports occur.
In this work, we analyze complex support pose transitions in human motion
involving locomotion and manipulation tasks (loco-manipulation). We have
applied a method for the detection of human support contacts from motion
capture data to a large-scale dataset of loco-manipulation motions involving
multi-contact supports, providing a semantic representation of them. Our
results provide a statistical analysis of the used support poses, their
transitions and the time spent in each of them. In addition, our data partially
validates our taxonomy of whole-body support poses presented in our previous
work.
We believe that this work extends our understanding of human motion for
humanoids, with a long-term objective of developing methods for autonomous
multi-contact motion planning.Comment: 8 pages, IEEE-RAS International Conference on Humanoid Robots
(Humanoids) 201
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