1,034 research outputs found
Learning to Singulate Objects using a Push Proposal Network
Learning to act in unstructured environments, such as cluttered piles of
objects, poses a substantial challenge for manipulation robots. We present a
novel neural network-based approach that separates unknown objects in clutter
by selecting favourable push actions. Our network is trained from data
collected through autonomous interaction of a PR2 robot with randomly organized
tabletop scenes. The model is designed to propose meaningful push actions based
on over-segmented RGB-D images. We evaluate our approach by singulating up to 8
unknown objects in clutter. We demonstrate that our method enables the robot to
perform the task with a high success rate and a low number of required push
actions. Our results based on real-world experiments show that our network is
able to generalize to novel objects of various sizes and shapes, as well as to
arbitrary object configurations. Videos of our experiments can be viewed at
http://robotpush.cs.uni-freiburg.deComment: International Symposium on Robotics Research (ISRR) 2017, videos:
http://robotpush.cs.uni-freiburg.d
Learning to Navigate Cloth using Haptics
We present a controller that allows an arm-like manipulator to navigate
deformable cloth garments in simulation through the use of haptic information.
The main challenge of such a controller is to avoid getting tangled in, tearing
or punching through the deforming cloth. Our controller aggregates force
information from a number of haptic-sensing spheres all along the manipulator
for guidance. Based on haptic forces, each individual sphere updates its target
location, and the conflicts that arise between this set of desired positions is
resolved by solving an inverse kinematic problem with constraints.
Reinforcement learning is used to train the controller for a single
haptic-sensing sphere, where a training run is terminated (and thus penalized)
when large forces are detected due to contact between the sphere and a
simplified model of the cloth. In simulation, we demonstrate successful
navigation of a robotic arm through a variety of garments, including an
isolated sleeve, a jacket, a shirt, and shorts. Our controller out-performs two
baseline controllers: one without haptics and another that was trained based on
large forces between the sphere and cloth, but without early termination.Comment: Supplementary video available at https://youtu.be/iHqwZPKVd4A.
Related publications http://www.cc.gatech.edu/~karenliu/Robotic_dressing.htm
The State of Lifelong Learning in Service Robots: Current Bottlenecks in Object Perception and Manipulation
Service robots are appearing more and more in our daily life. The development
of service robots combines multiple fields of research, from object perception
to object manipulation. The state-of-the-art continues to improve to make a
proper coupling between object perception and manipulation. This coupling is
necessary for service robots not only to perform various tasks in a reasonable
amount of time but also to continually adapt to new environments and safely
interact with non-expert human users. Nowadays, robots are able to recognize
various objects, and quickly plan a collision-free trajectory to grasp a target
object in predefined settings. Besides, in most of the cases, there is a
reliance on large amounts of training data. Therefore, the knowledge of such
robots is fixed after the training phase, and any changes in the environment
require complicated, time-consuming, and expensive robot re-programming by
human experts. Therefore, these approaches are still too rigid for real-life
applications in unstructured environments, where a significant portion of the
environment is unknown and cannot be directly sensed or controlled. In such
environments, no matter how extensive the training data used for batch
learning, a robot will always face new objects. Therefore, apart from batch
learning, the robot should be able to continually learn about new object
categories and grasp affordances from very few training examples on-site.
Moreover, apart from robot self-learning, non-expert users could interactively
guide the process of experience acquisition by teaching new concepts, or by
correcting insufficient or erroneous concepts. In this way, the robot will
constantly learn how to help humans in everyday tasks by gaining more and more
experiences without the need for re-programming
Toward Robots with Peripersonal Space Representation for Adaptive Behaviors
The abilities to adapt and act autonomously in an unstructured and
human-oriented environment are necessarily vital for the next generation of
robots, which aim to safely cooperate with humans. While this adaptability
is natural and feasible for humans, it is still very complex and challenging
for robots. Observations and findings from psychology and neuroscience in
respect to the development of the human sensorimotor system can inform
the development of novel approaches to adaptive robotics.
Among these is the formation of the representation of space closely surrounding
the body, the Peripersonal Space (PPS) , from multisensory sources
like vision, hearing, touch and proprioception, which helps to facilitate human
activities within their surroundings.
Taking inspiration from the virtual safety margin formed by the PPS representation
in humans, this thesis first constructs an equivalent model of the
safety zone for each body part of the iCub humanoid robot. This PPS layer
serves as a distributed collision predictor, which translates visually detected
objects approaching a robot\u2019s body parts (e.g., arm, hand) into the probabilities
of a collision between those objects and body parts. This leads to
adaptive avoidance behaviors in the robot via an optimization-based reactive
controller. Notably, this visual reactive control pipeline can also seamlessly
incorporate tactile input to guarantee safety in both pre- and post-collision
phases in physical Human-Robot Interaction (pHRI). Concurrently, the controller
is also able to take into account multiple targets (of manipulation reaching tasks) generated by a multiple Cartesian point planner. All components,
namely the PPS, the multi-target motion planner (for manipulation
reaching tasks), the reaching-with-avoidance controller and the humancentred
visual perception, are combined harmoniously to form a hybrid control
framework designed to provide safety for robots\u2019 interactions in a cluttered
environment shared with human partners.
Later, motivated by the development of manipulation skills in infants, in
which the multisensory integration is thought to play an important role, a
learning framework is proposed to allow a robot to learn the processes of
forming sensory representations, namely visuomotor and visuotactile, from
their own motor activities in the environment. Both multisensory integration
models are constructed with Deep Neural Networks (DNNs) in such a
way that their outputs are represented in motor space to facilitate the robot\u2019s
subsequent actions
Recommended from our members
Improving Robotic Manipulation via Reachability, Tactile, and Spatial Awareness
Robotic grasping and manipulation remains an active area of research despite significant progress over the past decades. Many existing solutions still struggle to robustly handle difficult situations that a robot might encounter even in non-contrived settings.For example, grasping systems struggle when the object is not centrally located in the robot's workspace. Also, grasping in dynamic environments presents a unique set of challenges. A stable and feasible grasp can become infeasible as the object moves; this problem becomes pronounced when there are obstacles in the scene.
This research is inspired by the observation that object-manipulation tasks like grasping, pick-and-place or insertion require different forms of awareness. These include reachability awareness -- being aware of regions that can be reached without self-collision or collision with surrounding objects; tactile awareness-- ability to feel and grasp objects just tight enough to prevent slippage or crushing the objects; and 3D awareness -- ability to perceive size and depth in ways that makes object manipulation possible. Humans use these capabilities to achieve a high level of coordination needed for object manipulation. In this work, we develop techniques that equip robots with similar sensitivities towards realizing a reliable and capable home-assistant robot.
In this thesis we demonstrate the importance of reasoning about the robot's workspace to enable grasping systems handle more difficult settings such as picking up moving objects while avoiding surrounding obstacles. Our method encodes the notion of reachability and uses it to generate not just stable grasps but ones that are also achievable by the robot. This reachability-aware formulation effectively expands the useable workspace of the robot enabling the robot to pick up objects from difficult-to-reach locations. While recent vision-based grasping systems work reliably well achieving pickup success rate higher than 90\% in cluttered scenes, failure cases due to calibration error, slippage and occlusion were challenging. To address this, we develop a closed-loop tactile-based improvement that uses additional tactile sensing to deal with self-occlusion (a limitation of vision-based system) and adaptively tighten the robot's grip on the object-- making the grasping system tactile-aware and more reliable. This can be used as an add-on to existing grasping systems.
This adaptive tactile-based approach demonstrates the effectiveness of closed-loop feedback in the final phase of the grasping process. To achieve closed-loop manipulation all through the manipulation process, we study the value of multi-view camera systems to improve learning-based manipulation systems.
Using a multi-view Q-learning formulation, we develop a learned closed-loop manipulation algorithm for precise manipulation tasks that integrates inputs from multiple static RGB cameras to overcome self-occlusion and improve 3D understanding.
To conclude, we discuss some opportunities/ directions for future work
- …