13,384 research outputs found
A probabilistic approach to robot trajectory generation
Motor Primitives (MPs) are a promising approach
for the data-driven acquisition as well as for the modular and
re-usable generation of movements. However, a modular control
architecture with MPs is only effective if the MPs support
co-activation as well as continuously blending the activation
from one MP to the next. In addition, we need efficient
mechanisms to adapt a MP to the current situation. Common
approaches to movement primitives lack such capabilities or
their implementation is based on heuristics. We present a
probabilistic movement primitive approach that overcomes the
limitations of existing approaches. We encode a primitive as a
probability distribution over trajectories. The representation as
distribution has several beneficial properties. It allows encoding
a time-varying variance profile. Most importantly, it allows
performing new operations — a product of distributions for
the co-activation of MPs conditioning for generalizing the MP
to different desired targets. We derive a feedback controller
that reproduces a given trajectory distribution in closed form.
We compare our approach to the existing state-of-the art and
present real robot results for learning from demonstration
Learning Human-Robot Collaboration Insights through the Integration of Muscle Activity in Interaction Motion Models
Recent progress in human-robot collaboration makes fast and fluid
interactions possible, even when human observations are partial and occluded.
Methods like Interaction Probabilistic Movement Primitives (ProMP) model human
trajectories through motion capture systems. However, such representation does
not properly model tasks where similar motions handle different objects. Under
current approaches, a robot would not adapt its pose and dynamics for proper
handling. We integrate the use of Electromyography (EMG) into the Interaction
ProMP framework and utilize muscular signals to augment the human observation
representation. The contribution of our paper is increased task discernment
when trajectories are similar but tools are different and require the robot to
adjust its pose for proper handling. Interaction ProMPs are used with an
augmented vector that integrates muscle activity. Augmented time-normalized
trajectories are used in training to learn correlation parameters and robot
motions are predicted by finding the best weight combination and temporal
scaling for a task. Collaborative single task scenarios with similar motions
but different objects were used and compared. For one experiment only joint
angles were recorded, for the other EMG signals were additionally integrated.
Task recognition was computed for both tasks. Observation state vectors with
augmented EMG signals were able to completely identify differences across
tasks, while the baseline method failed every time. Integrating EMG signals
into collaborative tasks significantly increases the ability of the system to
recognize nuances in the tasks that are otherwise imperceptible, up to 74.6% in
our studies. Furthermore, the integration of EMG signals for collaboration also
opens the door to a wide class of human-robot physical interactions based on
haptic communication that has been largely unexploited in the field.Comment: 7 pages, 2 figures, 2 tables. As submitted to Humanoids 201
Goal-Directed Planning for Habituated Agents by Active Inference Using a Variational Recurrent Neural Network
It is crucial to ask how agents can achieve goals by generating action plans
using only partial models of the world acquired through habituated
sensory-motor experiences. Although many existing robotics studies use a
forward model framework, there are generalization issues with high degrees of
freedom. The current study shows that the predictive coding (PC) and active
inference (AIF) frameworks, which employ a generative model, can develop better
generalization by learning a prior distribution in a low dimensional latent
state space representing probabilistic structures extracted from well
habituated sensory-motor trajectories. In our proposed model, learning is
carried out by inferring optimal latent variables as well as synaptic weights
for maximizing the evidence lower bound, while goal-directed planning is
accomplished by inferring latent variables for maximizing the estimated lower
bound. Our proposed model was evaluated with both simple and complex robotic
tasks in simulation, which demonstrated sufficient generalization in learning
with limited training data by setting an intermediate value for a
regularization coefficient. Furthermore, comparative simulation results show
that the proposed model outperforms a conventional forward model in
goal-directed planning, due to the learned prior confining the search of motor
plans within the range of habituated trajectories.Comment: 30 pages, 19 figure
Stochastic Sampling Simulation for Pedestrian Trajectory Prediction
Urban environments pose a significant challenge for autonomous vehicles (AVs)
as they must safely navigate while in close proximity to many pedestrians. It
is crucial for the AV to correctly understand and predict the future
trajectories of pedestrians to avoid collision and plan a safe path. Deep
neural networks (DNNs) have shown promising results in accurately predicting
pedestrian trajectories, relying on large amounts of annotated real-world data
to learn pedestrian behavior. However, collecting and annotating these large
real-world pedestrian datasets is costly in both time and labor. This paper
describes a novel method using a stochastic sampling-based simulation to train
DNNs for pedestrian trajectory prediction with social interaction. Our novel
simulation method can generate vast amounts of automatically-annotated,
realistic, and naturalistic synthetic pedestrian trajectories based on small
amounts of real annotation. We then use such synthetic trajectories to train an
off-the-shelf state-of-the-art deep learning approach Social GAN (Generative
Adversarial Network) to perform pedestrian trajectory prediction. Our proposed
architecture, trained only using synthetic trajectories, achieves better
prediction results compared to those trained on human-annotated real-world data
using the same network. Our work demonstrates the effectiveness and potential
of using simulation as a substitution for human annotation efforts to train
high-performing prediction algorithms such as the DNNs.Comment: 8 pages, 6 figures and 2 table
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