50,283 research outputs found
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
Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets
In this work, we explore the correlation between people trajectories and
their head orientations. We argue that people trajectory and head pose
forecasting can be modelled as a joint problem. Recent approaches on trajectory
forecasting leverage short-term trajectories (aka tracklets) of pedestrians to
predict their future paths. In addition, sociological cues, such as expected
destination or pedestrian interaction, are often combined with tracklets. In
this paper, we propose MiXing-LSTM (MX-LSTM) to capture the interplay between
positions and head orientations (vislets) thanks to a joint unconstrained
optimization of full covariance matrices during the LSTM backpropagation. We
additionally exploit the head orientations as a proxy for the visual attention,
when modeling social interactions. MX-LSTM predicts future pedestrians location
and head pose, increasing the standard capabilities of the current approaches
on long-term trajectory forecasting. Compared to the state-of-the-art, our
approach shows better performances on an extensive set of public benchmarks.
MX-LSTM is particularly effective when people move slowly, i.e. the most
challenging scenario for all other models. The proposed approach also allows
for accurate predictions on a longer time horizon.Comment: Accepted at IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE
INTELLIGENCE 2019. arXiv admin note: text overlap with arXiv:1805.0065
Discovery and recognition of motion primitives in human activities
We present a novel framework for the automatic discovery and recognition of
motion primitives in videos of human activities. Given the 3D pose of a human
in a video, human motion primitives are discovered by optimizing the `motion
flux', a quantity which captures the motion variation of a group of skeletal
joints. A normalization of the primitives is proposed in order to make them
invariant with respect to a subject anatomical variations and data sampling
rate. The discovered primitives are unknown and unlabeled and are
unsupervisedly collected into classes via a hierarchical non-parametric Bayes
mixture model. Once classes are determined and labeled they are further
analyzed for establishing models for recognizing discovered primitives. Each
primitive model is defined by a set of learned parameters.
Given new video data and given the estimated pose of the subject appearing on
the video, the motion is segmented into primitives, which are recognized with a
probability given according to the parameters of the learned models.
Using our framework we build a publicly available dataset of human motion
primitives, using sequences taken from well-known motion capture datasets. We
expect that our framework, by providing an objective way for discovering and
categorizing human motion, will be a useful tool in numerous research fields
including video analysis, human inspired motion generation, learning by
demonstration, intuitive human-robot interaction, and human behavior analysis
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