1,432 research outputs found
Learning Task Priorities from Demonstrations
Bimanual operations in humanoids offer the possibility to carry out more than
one manipulation task at the same time, which in turn introduces the problem of
task prioritization. We address this problem from a learning from demonstration
perspective, by extending the Task-Parameterized Gaussian Mixture Model
(TP-GMM) to Jacobian and null space structures. The proposed approach is tested
on bimanual skills but can be applied in any scenario where the prioritization
between potentially conflicting tasks needs to be learned. We evaluate the
proposed framework in: two different tasks with humanoids requiring the
learning of priorities and a loco-manipulation scenario, showing that the
approach can be exploited to learn the prioritization of multiple tasks in
parallel.Comment: Accepted for publication at the IEEE Transactions on Robotic
Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks
Autonomous robots need to interact with unknown, unstructured and changing
environments, constantly facing novel challenges. Therefore, continuous online
adaptation for lifelong-learning and the need of sample-efficient mechanisms to
adapt to changes in the environment, the constraints, the tasks, or the robot
itself are crucial. In this work, we propose a novel framework for
probabilistic online motion planning with online adaptation based on a
bio-inspired stochastic recurrent neural network. By using learning signals
which mimic the intrinsic motivation signalcognitive dissonance in addition
with a mental replay strategy to intensify experiences, the stochastic
recurrent network can learn from few physical interactions and adapts to novel
environments in seconds. We evaluate our online planning and adaptation
framework on an anthropomorphic KUKA LWR arm. The rapid online adaptation is
shown by learning unknown workspace constraints sample-efficiently from few
physical interactions while following given way points.Comment: accepted in Neural Network
Habitat 3.0: A Co-Habitat for Humans, Avatars and Robots
We present Habitat 3.0: a simulation platform for studying collaborative
human-robot tasks in home environments. Habitat 3.0 offers contributions across
three dimensions: (1) Accurate humanoid simulation: addressing challenges in
modeling complex deformable bodies and diversity in appearance and motion, all
while ensuring high simulation speed. (2) Human-in-the-loop infrastructure:
enabling real human interaction with simulated robots via mouse/keyboard or a
VR interface, facilitating evaluation of robot policies with human input. (3)
Collaborative tasks: studying two collaborative tasks, Social Navigation and
Social Rearrangement. Social Navigation investigates a robot's ability to
locate and follow humanoid avatars in unseen environments, whereas Social
Rearrangement addresses collaboration between a humanoid and robot while
rearranging a scene. These contributions allow us to study end-to-end learned
and heuristic baselines for human-robot collaboration in-depth, as well as
evaluate them with humans in the loop. Our experiments demonstrate that learned
robot policies lead to efficient task completion when collaborating with unseen
humanoid agents and human partners that might exhibit behaviors that the robot
has not seen before. Additionally, we observe emergent behaviors during
collaborative task execution, such as the robot yielding space when obstructing
a humanoid agent, thereby allowing the effective completion of the task by the
humanoid agent. Furthermore, our experiments using the human-in-the-loop tool
demonstrate that our automated evaluation with humanoids can provide an
indication of the relative ordering of different policies when evaluated with
real human collaborators. Habitat 3.0 unlocks interesting new features in
simulators for Embodied AI, and we hope it paves the way for a new frontier of
embodied human-AI interaction capabilities.Comment: Project page: http://aihabitat.org/habitat
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