780 research outputs found
Online Visual Robot Tracking and Identification using Deep LSTM Networks
Collaborative robots working on a common task are necessary for many
applications. One of the challenges for achieving collaboration in a team of
robots is mutual tracking and identification. We present a novel pipeline for
online visionbased detection, tracking and identification of robots with a
known and identical appearance. Our method runs in realtime on the limited
hardware of the observer robot. Unlike previous works addressing robot tracking
and identification, we use a data-driven approach based on recurrent neural
networks to learn relations between sequential inputs and outputs. We formulate
the data association problem as multiple classification problems. A deep LSTM
network was trained on a simulated dataset and fine-tuned on small set of real
data. Experiments on two challenging datasets, one synthetic and one real,
which include long-term occlusions, show promising results.Comment: IEEE/RSJ International Conference on Intelligent Robots and Systems
(IROS), Vancouver, Canada, 2017. IROS RoboCup Best Paper Awar
Predicting human motion intention for pHRI assistive control
This work addresses human intention identification during physical
Human-Robot Interaction (pHRI) tasks to include this information in an
assistive controller. To this purpose, human intention is defined as the
desired trajectory that the human wants to follow over a finite rolling
prediction horizon so that the robot can assist in pursuing it. This work
investigates a Recurrent Neural Network (RNN), specifically, Long-Short Term
Memory (LSTM) cascaded with a Fully Connected layer. In particular, we propose
an iterative training procedure to adapt the model. Such an iterative procedure
is powerful in reducing the prediction error. Still, it has the drawback that
it is time-consuming and does not generalize to different users or different
co-manipulated objects. To overcome this issue, Transfer Learning (TL) adapts
the pre-trained model to new trajectories, users, and co-manipulated objects by
freezing the LSTM layer and fine-tuning the last FC layer, which makes the
procedure faster. Experiments show that the iterative procedure adapts the
model and reduces prediction error. Experiments also show that TL adapts to
different users and to the co-manipulation of a large object. Finally, to check
the utility of adopting the proposed method, we compare the proposed controller
enhanced by the intention prediction with the other two standard controllers of
pHRI
Anticipating Daily Intention using On-Wrist Motion Triggered Sensing
Anticipating human intention by observing one's actions has many
applications. For instance, picking up a cellphone, then a charger (actions)
implies that one wants to charge the cellphone (intention). By anticipating the
intention, an intelligent system can guide the user to the closest power
outlet. We propose an on-wrist motion triggered sensing system for anticipating
daily intentions, where the on-wrist sensors help us to persistently observe
one's actions. The core of the system is a novel Recurrent Neural Network (RNN)
and Policy Network (PN), where the RNN encodes visual and motion observation to
anticipate intention, and the PN parsimoniously triggers the process of visual
observation to reduce computation requirement. We jointly trained the whole
network using policy gradient and cross-entropy loss. To evaluate, we collect
the first daily "intention" dataset consisting of 2379 videos with 34
intentions and 164 unique action sequences. Our method achieves 92.68%, 90.85%,
97.56% accuracy on three users while processing only 29% of the visual
observation on average
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