106,722 research outputs found
Unsupervised Learning of Scene Flow
As Computer Vision-powered autonomous systems are increasingly deployed to solve problems in the wild, the case is made for developing visual understanding methods that are robust and flexible.
One of the most challenging tasks for this purpose is given by the extraction of scene flow, that is the dense three-dimensional vector field that associates each world point with its corresponding position in the next observed frame, hence describing its three-dimensional motion entirely. The recent addition of a limited amount of ground truth scene flow information to the popular KITTI dataset prompted a renewed interest in the study of techniques for scene flow inference, although the proposed solutions in literature mostly rely on computation-intensive techniques and are characterised by execution times that are not suited for real-time application.
In the wake of the recent widespread adoption of Deep Learning techniques to Computer Vision tasks and in light of the convenience of Unsupervised Learning for scenarios in which ground truth collection is difficult and time-consuming, this thesis work proposes the first neural network architecture to be trained in end-to-end fashion for unsupervised scene flow regression from monocular visual data, called Pantaflow. The proposed solution is much faster than currently available state-of-the-art methods and therefore represents a step towards the achievement of real-time scene flow inference
Conditional Task and Motion Planning through an Effort-based Approach
This paper proposes a preliminary work on a Conditional Task and Motion
Planning algorithm able to find a plan that minimizes robot efforts while
solving assigned tasks. Unlike most of the existing approaches that replan a
path only when it becomes unfeasible (e.g., no collision-free paths exist), the
proposed algorithm takes into consideration a replanning procedure whenever an
effort-saving is possible. The effort is here considered as the execution time,
but it is extensible to the robot energy consumption. The computed plan is both
conditional and dynamically adaptable to the unexpected environmental changes.
Based on the theoretical analysis of the algorithm, authors expect their
proposal to be complete and scalable. In progress experiments aim to prove this
investigation
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
Recovering from External Disturbances in Online Manipulation through State-Dependent Revertive Recovery Policies
Robots are increasingly entering uncertain and unstructured environments.
Within these, robots are bound to face unexpected external disturbances like
accidental human or tool collisions. Robots must develop the capacity to
respond to unexpected events. That is not only identifying the sudden anomaly,
but also deciding how to handle it. In this work, we contribute a recovery
policy that allows a robot to recovery from various anomalous scenarios across
different tasks and conditions in a consistent and robust fashion. The system
organizes tasks as a sequence of nodes composed of internal modules such as
motion generation and introspection. When an introspection module flags an
anomaly, the recovery strategy is triggered and reverts the task execution by
selecting a target node as a function of a state dependency chart. The new
skill allows the robot to overcome the effects of the external disturbance and
conclude the task. Our system recovers from accidental human and tool
collisions in a number of tasks. Of particular importance is the fact that we
test the robustness of the recovery system by triggering anomalies at each node
in the task graph showing robust recovery everywhere in the task. We also
trigger multiple and repeated anomalies at each of the nodes of the task
showing that the recovery system can consistently recover anywhere in the
presence of strong and pervasive anomalous conditions. Robust recovery systems
will be key enablers for long-term autonomy in robot systems. Supplemental info
including code, data, graphs, and result analysis can be found at [1].Comment: 8 pages, 8 figures, 1 tabl
Decentralized Motion Planning with Collision Avoidance for a Team of UAVs under High Level Goals
This paper addresses the motion planning problem for a team of aerial agents
under high level goals. We propose a hybrid control strategy that guarantees
the accomplishment of each agent's local goal specification, which is given as
a temporal logic formula, while guaranteeing inter-agent collision avoidance.
In particular, by defining 3-D spheres that bound the agents' volume, we extend
previous work on decentralized navigation functions and propose control laws
that navigate the agents among predefined regions of interest of the workspace
while avoiding collision with each other. This allows us to abstract the motion
of the agents as finite transition systems and, by employing standard formal
verification techniques, to derive a high-level control algorithm that
satisfies the agents' specifications. Simulation and experimental results with
quadrotors verify the validity of the proposed method.Comment: Submitted to the IEEE International Conference on Robotics and
Automation (ICRA), Singapore, 201
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