1,562 research outputs found
Motion Imitation Based on Sparsely Sampled Correspondence
Existing techniques for motion imitation often suffer a certain level of
latency due to their computational overhead or a large set of correspondence
samples to search. To achieve real-time imitation with small latency, we
present a framework in this paper to reconstruct motion on humanoids based on
sparsely sampled correspondence. The imitation problem is formulated as finding
the projection of a point from the configuration space of a human's poses into
the configuration space of a humanoid. An optimal projection is defined as the
one that minimizes a back-projected deviation among a group of candidates,
which can be determined in a very efficient way. Benefited from this
formulation, effective projections can be obtained by using sparse
correspondence. Methods for generating these sparse correspondence samples have
also been introduced. Our method is evaluated by applying the human's motion
captured by a RGB-D sensor to a humanoid in real-time. Continuous motion can be
realized and used in the example application of tele-operation.Comment: 8 pages, 8 figures, technical repor
SA-Net: Deep Neural Network for Robot Trajectory Recognition from RGB-D Streams
Learning from demonstration (LfD) and imitation learning offer new paradigms
for transferring task behavior to robots. A class of methods that enable such
online learning require the robot to observe the task being performed and
decompose the sensed streaming data into sequences of state-action pairs, which
are then input to the methods. Thus, recognizing the state-action pairs
correctly and quickly in sensed data is a crucial prerequisite for these
methods. We present SA-Net a deep neural network architecture that recognizes
state-action pairs from RGB-D data streams. SA-Net performed well in two
diverse robotic applications of LfD -- one involving mobile ground robots and
another involving a robotic manipulator -- which demonstrates that the
architecture generalizes well to differing contexts. Comprehensive evaluations
including deployment on a physical robot show that \sanet{} significantly
improves on the accuracy of the previous method that utilizes traditional image
processing and segmentation.Comment: (in press
Learning Social Affordance Grammar from Videos: Transferring Human Interactions to Human-Robot Interactions
In this paper, we present a general framework for learning social affordance
grammar as a spatiotemporal AND-OR graph (ST-AOG) from RGB-D videos of human
interactions, and transfer the grammar to humanoids to enable a real-time
motion inference for human-robot interaction (HRI). Based on Gibbs sampling,
our weakly supervised grammar learning can automatically construct a
hierarchical representation of an interaction with long-term joint sub-tasks of
both agents and short term atomic actions of individual agents. Based on a new
RGB-D video dataset with rich instances of human interactions, our experiments
of Baxter simulation, human evaluation, and real Baxter test demonstrate that
the model learned from limited training data successfully generates human-like
behaviors in unseen scenarios and outperforms both baselines.Comment: The 2017 IEEE International Conference on Robotics and Automation
(ICRA
Long-term use of motion-based video games in care home settings
Recent research suggests that motion-based video games have the potential to provide both mental and physical stimulation for older adults in residential care. However, little research has explored the practical challenges and opportunities that arise from integrating these games within existing schedules of activities in these contexts. In our work, we report on a qualitative enquiry that was conducted over a three month period at two long-term care facilities. Findings suggest that older adults enjoyed playing video games, and that games can be a valuable means of re-introducing challenge in late life, but that the impact of age-related changes and impairment can influence people’s ability to engage with games in a group setting. We outline core challenges in the design for care context and discuss implications of our work regarding the suitability of games as a self-directed leisure activity
Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation
Manipulation of deformable objects, such as ropes and cloth, is an important
but challenging problem in robotics. We present a learning-based system where a
robot takes as input a sequence of images of a human manipulating a rope from
an initial to goal configuration, and outputs a sequence of actions that can
reproduce the human demonstration, using only monocular images as input. To
perform this task, the robot learns a pixel-level inverse dynamics model of
rope manipulation directly from images in a self-supervised manner, using about
60K interactions with the rope collected autonomously by the robot. The human
demonstration provides a high-level plan of what to do and the low-level
inverse model is used to execute the plan. We show that by combining the high
and low-level plans, the robot can successfully manipulate a rope into a
variety of target shapes using only a sequence of human-provided images for
direction.Comment: 8 pages, accepted to International Conference on Robotics and
Automation (ICRA) 201
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