2,569 research outputs found
Using humanoid robots to study human behavior
Our understanding of human behavior advances as our humanoid robotics work progresses-and vice versa. This team's work focuses on trajectory formation and planning, learning from demonstration, oculomotor control and interactive behaviors. They are programming robotic behavior based on how we humans “program” behavior in-or train-each other
Real-time fuzzy inference based robot path planning
This project addresses the problem of adaptive trajectory generation for a robot arm. Conventional trajectory generation involves computing a path in real time to minimize a performance measure such as expended energy. This method can be computationally intensive, and it may yield poor results if the trajectory is weakly constrained. Typically some implicit constraints are known, but cannot be encoded analytically. The alternative approach used here is to formulate domain-specific knowledge, including implicit and ill-defined constraints, in terms of fuzzy rules. These rules utilize linguistic terms to relate input variables to output variables. Since the fuzzy rulebase is determined off-line, only high-level, computationally light processing is required in real time. Potential applications for adaptive trajectory generation include missile guidance and various sophisticated robot control tasks, such as automotive assembly, high speed electrical parts insertion, stepper alignment, and motion control for high speed parcel transfer systems
Machine Learning-based Framework for Optimally Solving the Analytical Inverse Kinematics for Redundant Manipulators
Solving the analytical inverse kinematics (IK) of redundant manipulators in
real time is a difficult problem in robotics since its solution for a given
target pose is not unique. Moreover, choosing the optimal IK solution with
respect to application-specific demands helps to improve the robustness and to
increase the success rate when driving the manipulator from its current
configuration towards a desired pose. This is necessary, especially in
high-dynamic tasks like catching objects in mid-flights. To compute a suitable
target configuration in the joint space for a given target pose in the
trajectory planning context, various factors such as travel time or
manipulability must be considered. However, these factors increase the
complexity of the overall problem which impedes real-time implementation. In
this paper, a real-time framework to compute the analytical inverse kinematics
of a redundant robot is presented. To this end, the analytical IK of the
redundant manipulator is parameterized by so-called redundancy parameters,
which are combined with a target pose to yield a unique IK solution. Most
existing works in the literature either try to approximate the direct mapping
from the desired pose of the manipulator to the solution of the IK or cluster
the entire workspace to find IK solutions. In contrast, the proposed framework
directly learns these redundancy parameters by using a neural network (NN) that
provides the optimal IK solution with respect to the manipulability and the
closeness to the current robot configuration. Monte Carlo simulations show the
effectiveness of the proposed approach which is accurate and real-time capable
( \SI{32}{\micro\second}) on the KUKA LBR iiwa 14 R820
PAMPC: Perception-Aware Model Predictive Control for Quadrotors
We present the first perception-aware model predictive control framework for
quadrotors that unifies control and planning with respect to action and
perception objectives. Our framework leverages numerical optimization to
compute trajectories that satisfy the system dynamics and require control
inputs within the limits of the platform. Simultaneously, it optimizes
perception objectives for robust and reliable sens- ing by maximizing the
visibility of a point of interest and minimizing its velocity in the image
plane. Considering both perception and action objectives for motion planning
and control is challenging due to the possible conflicts arising from their
respective requirements. For example, for a quadrotor to track a reference
trajectory, it needs to rotate to align its thrust with the direction of the
desired acceleration. However, the perception objective might require to
minimize such rotation to maximize the visibility of a point of interest. A
model-based optimization framework, able to consider both perception and action
objectives and couple them through the system dynamics, is therefore necessary.
Our perception-aware model predictive control framework works in a
receding-horizon fashion by iteratively solving a non-linear optimization
problem. It is capable of running in real-time, fully onboard our lightweight,
small-scale quadrotor using a low-power ARM computer, to- gether with a
visual-inertial odometry pipeline. We validate our approach in experiments
demonstrating (I) the contradiction between perception and action objectives,
and (II) improved behavior in extremely challenging lighting conditions
Collaborative Planning for Catching and Transporting Objects in Unstructured Environments
Multi-robot teams have attracted attention from industry and academia for
their ability to perform collaborative tasks in unstructured environments, such
as wilderness rescue and collaborative transportation.In this paper, we propose
a trajectory planning method for a non-holonomic robotic team with
collaboration in unstructured environments.For the adaptive state collaboration
of a robot team to catch and transport targets to be rescued using a net, we
model the process of catching the falling target with a net in a continuous and
differentiable form.This enables the robot team to fully exploit the kinematic
potential, thereby adaptively catching the target in an appropriate
state.Furthermore, the size safety and topological safety of the net, resulting
from the collaborative support of the robots, are guaranteed through geometric
constraints.We integrate our algorithm on a car-like robot team and test it in
simulations and real-world experiments to validate our performance.Our method
is compared to state-of-the-art multi-vehicle trajectory planning methods,
demonstrating significant performance in efficiency and trajectory quality
Impact-Friendly Object Catching at Non-Zero Velocity Based on Combined Optimization and Learning
This paper proposes a combined optimization and learning method for
impact-friendly, non-prehensile catching of objects at non-zero velocity.
Through a constrained Quadratic Programming problem, the method generates
optimal trajectories up to the contact point between the robot and the object
to minimize their relative velocity and reduce the impact forces. Next, the
generated trajectories are updated by Kernelized Movement Primitives, which are
based on human catching demonstrations to ensure a smooth transition around the
catching point. In addition, the learned human variable stiffness (HVS) is sent
to the robot's Cartesian impedance controller to absorb the post-impact forces
and stabilize the catching position. Three experiments are conducted to compare
our method with and without HVS against a fixed-position impedance controller
(FP-IC). The results showed that the proposed methods outperform the FP-IC
while adding HVS yields better results for absorbing the post-impact forces.Comment: 8 pages, 9 figures, accepted by 2023 IEEE/RSJ International
Conference on Intelligent Robots and Systems (IROS 2023
Deep Haptic Model Predictive Control for Robot-Assisted Dressing
Robot-assisted dressing offers an opportunity to benefit the lives of many
people with disabilities, such as some older adults. However, robots currently
lack common sense about the physical implications of their actions on people.
The physical implications of dressing are complicated by non-rigid garments,
which can result in a robot indirectly applying high forces to a person's body.
We present a deep recurrent model that, when given a proposed action by the
robot, predicts the forces a garment will apply to a person's body. We also
show that a robot can provide better dressing assistance by using this model
with model predictive control. The predictions made by our model only use
haptic and kinematic observations from the robot's end effector, which are
readily attainable. Collecting training data from real world physical
human-robot interaction can be time consuming, costly, and put people at risk.
Instead, we train our predictive model using data collected in an entirely
self-supervised fashion from a physics-based simulation. We evaluated our
approach with a PR2 robot that attempted to pull a hospital gown onto the arms
of 10 human participants. With a 0.2s prediction horizon, our controller
succeeded at high rates and lowered applied force while navigating the garment
around a persons fist and elbow without getting caught. Shorter prediction
horizons resulted in significantly reduced performance with the sleeve catching
on the participants' fists and elbows, demonstrating the value of our model's
predictions. These behaviors of mitigating catches emerged from our deep
predictive model and the controller objective function, which primarily
penalizes high forces.Comment: 8 pages, 12 figures, 1 table, 2018 IEEE International Conference on
Robotics and Automation (ICRA
Efficient motion planning for problems lacking optimal substructure
We consider the motion-planning problem of planning a collision-free path of
a robot in the presence of risk zones. The robot is allowed to travel in these
zones but is penalized in a super-linear fashion for consecutive accumulative
time spent there. We suggest a natural cost function that balances path length
and risk-exposure time. Specifically, we consider the discrete setting where we
are given a graph, or a roadmap, and we wish to compute the minimal-cost path
under this cost function. Interestingly, paths defined using our cost function
do not have an optimal substructure. Namely, subpaths of an optimal path are
not necessarily optimal. Thus, the Bellman condition is not satisfied and
standard graph-search algorithms such as Dijkstra cannot be used. We present a
path-finding algorithm, which can be seen as a natural generalization of
Dijkstra's algorithm. Our algorithm runs in time, where~ and are the number of vertices and
edges of the graph, respectively, and is the number of intersections
between edges and the boundary of the risk zone. We present simulations on
robotic platforms demonstrating both the natural paths produced by our cost
function and the computational efficiency of our algorithm
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