249 research outputs found
Deep learning: creating bridges between DMPs in autoencoders and recurrent neural networks
The complexity in modeling human movement increases as the dimensionality of these movement grows.
Since searching more precision and flexibility involves more variables in the model. Dynamic Movement
Primitives (DMP) have shown the ability to generate joint movements with high complexity. However, the
problem remains in the interaction between several joints since DMP alone is not able to deal with it. To solve
this problem a new model called autoencoded dynamic movement primitive (AE- DMP) is introduced in the
work "Efficient movement representation by embedding DynamicMovement Primitives in Deep Autoencoders"[2].
The proposed approach uses autoencoder in order to find a representation of the movement in a latent space.
Consequently, the DMPmodel is able to reconstruct the complete movement. In thisMaster Thesis we will study
the implementation of this model and study its performance. All the features stated in the original paper are
checked, as multiple movements, sparsity and reconstruction of missing or corrupted data
Deep Predictive Policy Training using Reinforcement Learning
Skilled robot task learning is best implemented by predictive action policies
due to the inherent latency of sensorimotor processes. However, training such
predictive policies is challenging as it involves finding a trajectory of motor
activations for the full duration of the action. We propose a data-efficient
deep predictive policy training (DPPT) framework with a deep neural network
policy architecture which maps an image observation to a sequence of motor
activations. The architecture consists of three sub-networks referred to as the
perception, policy and behavior super-layers. The perception and behavior
super-layers force an abstraction of visual and motor data trained with
synthetic and simulated training samples, respectively. The policy super-layer
is a small sub-network with fewer parameters that maps data in-between the
abstracted manifolds. It is trained for each task using methods for policy
search reinforcement learning. We demonstrate the suitability of the proposed
architecture and learning framework by training predictive policies for skilled
object grasping and ball throwing on a PR2 robot. The effectiveness of the
method is illustrated by the fact that these tasks are trained using only about
180 real robot attempts with qualitative terminal rewards.Comment: This work is submitted to IEEE/RSJ International Conference on
Intelligent Robots and Systems 2017 (IROS2017
Neural probabilistic motor primitives for humanoid control
We focus on the problem of learning a single motor module that can flexibly
express a range of behaviors for the control of high-dimensional physically
simulated humanoids. To do this, we propose a motor architecture that has the
general structure of an inverse model with a latent-variable bottleneck. We
show that it is possible to train this model entirely offline to compress
thousands of expert policies and learn a motor primitive embedding space. The
trained neural probabilistic motor primitive system can perform one-shot
imitation of whole-body humanoid behaviors, robustly mimicking unseen
trajectories. Additionally, we demonstrate that it is also straightforward to
train controllers to reuse the learned motor primitive space to solve tasks,
and the resulting movements are relatively naturalistic. To support the
training of our model, we compare two approaches for offline policy cloning,
including an experience efficient method which we call linear feedback policy
cloning. We encourage readers to view a supplementary video (
https://youtu.be/CaDEf-QcKwA ) summarizing our results.Comment: Accepted as a conference paper at ICLR 201
MILD: Multimodal Interactive Latent Dynamics for Learning Human-Robot Interaction
Modeling interaction dynamics to generate robot trajectories that enable a
robot to adapt and react to a human's actions and intentions is critical for
efficient and effective collaborative Human-Robot Interactions (HRI). Learning
from Demonstration (LfD) methods from Human-Human Interactions (HHI) have shown
promising results, especially when coupled with representation learning
techniques. However, such methods for learning HRI either do not scale well to
high dimensional data or cannot accurately adapt to changing via-poses of the
interacting partner. We propose Multimodal Interactive Latent Dynamics (MILD),
a method that couples deep representation learning and probabilistic machine
learning to address the problem of two-party physical HRIs. We learn the
interaction dynamics from demonstrations, using Hidden Semi-Markov Models
(HSMMs) to model the joint distribution of the interacting agents in the latent
space of a Variational Autoencoder (VAE). Our experimental evaluations for
learning HRI from HHI demonstrations show that MILD effectively captures the
multimodality in the latent representations of HRI tasks, allowing us to decode
the varying dynamics occurring in such tasks. Compared to related work, MILD
generates more accurate trajectories for the controlled agent (robot) when
conditioned on the observed agent's (human) trajectory. Notably, MILD can learn
directly from camera-based pose estimations to generate trajectories, which we
then map to a humanoid robot without the need for any additional training.Comment: Accepted at the IEEE-RAS International Conference on Humanoid Robots
(Humanoids) 202
Isometric Motion Manifold Primitives
The Motion Manifold Primitive (MMP) produces, for a given task, a continuous
manifold of trajectories each of which can successfully complete the task. It
consists of the decoder function that parametrizes the manifold and the
probability density in the latent coordinate space. In this paper, we first
show that the MMP performance can significantly degrade due to the geometric
distortion in the latent space -- by distortion, we mean that similar motions
are not located nearby in the latent space. We then propose {\it Isometric
Motion Manifold Primitives (IMMP)} whose latent coordinate space preserves the
geometry of the manifold. For this purpose, we formulate and use a Riemannian
metric for the motion space (i.e., parametric curve space), which we call a
{\it CurveGeom Riemannian metric}. Experiments with planar obstacle-avoiding
motions and pushing manipulation tasks show that IMMP significantly outperforms
existing MMP methods. Code is available at
https://github.com/Gabe-YHLee/IMMP-public.Comment: 8 pages, 13 figures. This work has been submitted to the IEEE for
possible publicatio
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