129 research outputs found
Learning Barrier Functions for Constrained Motion Planning with Dynamical Systems
Stable dynamical systems are a flexible tool to plan robotic motions in
real-time. In the robotic literature, dynamical system motions are typically
planned without considering possible limitations in the robot's workspace. This
work presents a novel approach to learn workspace constraints from human
demonstrations and to generate motion trajectories for the robot that lie in
the constrained workspace. Training data are incrementally clustered into
different linear subspaces and used to fit a low dimensional representation of
each subspace. By considering the learned constraint subspaces as zeroing
barrier functions, we are able to design a control input that keeps the system
trajectory within the learned bounds. This control input is effectively
combined with the original system dynamics preserving eventual asymptotic
properties of the unconstrained system. Simulations and experiments on a real
robot show the effectiveness of the proposed approach
Point-to-Pose Voting based Hand Pose Estimation using Residual Permutation Equivariant Layer
Recently, 3D input data based hand pose estimation methods have shown
state-of-the-art performance, because 3D data capture more spatial information
than the depth image. Whereas 3D voxel-based methods need a large amount of
memory, PointNet based methods need tedious preprocessing steps such as
K-nearest neighbour search for each point. In this paper, we present a novel
deep learning hand pose estimation method for an unordered point cloud. Our
method takes 1024 3D points as input and does not require additional
information. We use Permutation Equivariant Layer (PEL) as the basic element,
where a residual network version of PEL is proposed for the hand pose
estimation task. Furthermore, we propose a voting based scheme to merge
information from individual points to the final pose output. In addition to the
pose estimation task, the voting-based scheme can also provide point cloud
segmentation result without ground-truth for segmentation. We evaluate our
method on both NYU dataset and the Hands2017Challenge dataset. Our method
outperforms recent state-of-the-art methods, where our pose accuracy is
currently the best for the Hands2017Challenge dataset
Oscillation Damping Control of Pendulum-like Manipulation Platform using Moving Masses
This paper presents an approach to damp out the oscillatory motion of the
pendulum-like hanging platform on which a robotic manipulator is mounted. To
this end, moving masses were installed on top of the platform. In this paper,
asymptotic stability of the platform (which implies oscillation damping) is
achieved by designing reference acceleration of the moving masses properly. A
main feature of this work is that we can achieve asymptotic stability of not
only the platform, but also the moving masses, which may be challenging due to
the under-actuation nature. The proposed scheme is validated by the simulation
studies.Comment: IFAC Symposium on Robot Control (SYROCO) 201
Merging Position and Orientation Motion Primitives
In this paper, we focus on generating complex robotic trajectories by merging
sequential motion primitives. A robotic trajectory is a time series of
positions and orientations ending at a desired target. Hence, we first discuss
the generation of converging pose trajectories via dynamical systems, providing
a rigorous stability analysis. Then, we present approaches to merge motion
primitives which represent both the position and the orientation part of the
motion. Developed approaches preserve the shape of each learned movement and
allow for continuous transitions among succeeding motion primitives. Presented
methodologies are theoretically described and experimentally evaluated, showing
that it is possible to generate a smooth pose trajectory out of multiple motion
primitives
Long-Horizon Task Planning and Execution with Functional Object-Oriented Networks
Following work on joint object-action representation, functional
object-oriented networks (FOON) were introduced as a knowledge representation
for robots. A FOON contains symbolic (high-level) concepts useful to a robot's
understanding of tasks and its environment for object-level planning. Prior to
this work, little has been done to show how plans acquired from FOON can be
executed by a robot, as the concepts in a FOON are too abstract for immediate
execution. We propose a hierarchical task planning approach that translates a
FOON graph into a PDDL-based representation of domain knowledge for task
planning and execution. As a result of this process, a task plan can be
acquired, which can be executed by a robot from start to end, leveraging the
use of action contexts and skills as dynamic movement primitives (DMPs). We
demonstrate the entire pipeline from planning to execution using CoppeliaSim
and show how learned action contexts can be extended to never-before-seen
scenarios.Comment: Preliminary Draft, 8 pages, IEEE Conference Forma
HOI4ABOT: Human-Object Interaction Anticipation for Human Intention Reading Collaborative roBOTs
Robots are becoming increasingly integrated into our lives, assisting us in
various tasks. To ensure effective collaboration between humans and robots, it
is essential that they understand our intentions and anticipate our actions. In
this paper, we propose a Human-Object Interaction (HOI) anticipation framework
for collaborative robots. We propose an efficient and robust transformer-based
model to detect and anticipate HOIs from videos. This enhanced anticipation
empowers robots to proactively assist humans, resulting in more efficient and
intuitive collaborations. Our model outperforms state-of-the-art results in HOI
detection and anticipation in VidHOI dataset with an increase of 1.76% and
1.04% in mAP respectively while being 15.4 times faster. We showcase the
effectiveness of our approach through experimental results in a real robot,
demonstrating that the robot's ability to anticipate HOIs is key for better
Human-Robot Interaction. More information can be found on our project webpage:
https://evm7.github.io/HOI4ABOT_page/Comment: Proceedings in Conference on Robot Learning 202
Generalization of Optimal Motion Trajectories for Bipedal Walking
Abstract— Control of robot locomotion profits from the use of pre-planned trajectories. This paper presents a way to generalize globally optimal and
dynamically consistent trajectories for cyclic bipedal walking. A small task-space consisting of stride-length and step time is mapped to spline parameters which fully define the optimal joint space motion. The paper presents the impact of different machine learning algorithms for velocity and torque optimal trajectories with respect to optimality and feasibility. To demonstrate the usefulness of the trajectories, a control approach is presented that allows general walking including transitions between points in the task-space
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