30 research outputs found
SwingBot: Learning Physical Features from In-hand Tactile Exploration for Dynamic Swing-up Manipulation
Several robot manipulation tasks are extremely sensitive to variations of the
physical properties of the manipulated objects. One such task is manipulating
objects by using gravity or arm accelerations, increasing the importance of
mass, center of mass, and friction information. We present SwingBot, a robot
that is able to learn the physical features of a held object through tactile
exploration. Two exploration actions (tilting and shaking) provide the tactile
information used to create a physical feature embedding space. With this
embedding, SwingBot is able to predict the swing angle achieved by a robot
performing dynamic swing-up manipulations on a previously unseen object. Using
these predictions, it is able to search for the optimal control parameters for
a desired swing-up angle. We show that with the learned physical features our
end-to-end self-supervised learning pipeline is able to substantially improve
the accuracy of swinging up unseen objects. We also show that objects with
similar dynamics are closer to each other on the embedding space and that the
embedding can be disentangled into values of specific physical properties.Comment: IROS 202
Haptic search with the Smart Suction Cup on adversarial objects
Suction cups are an important gripper type in industrial robot applications,
and prior literature focuses on using vision-based planners to improve grasping
success in these tasks. Vision-based planners can fail due to adversarial
objects or lose generalizability for unseen scenarios, without retraining
learned algorithms. We propose haptic exploration to improve suction cup
grasping when visual grasp planners fail. We present the Smart Suction Cup, an
end-effector that utilizes internal flow measurements for tactile sensing. We
show that model-based haptic search methods, guided by these flow measurements,
improve grasping success by up to 2.5x as compared with using only a vision
planner during a bin-picking task. In characterizing the Smart Suction Cup on
both geometric edges and curves, we find that flow rate can accurately predict
the ideal motion direction even with large postural errors. The Smart Suction
Cup includes no electronics on the cup itself, such that the design is easy to
fabricate and haptic exploration does not damage the sensor. This work
motivates the use of suction cups with autonomous haptic search capabilities in
especially adversarial scenarios
Learning cloth manipulation with demonstrations
Recent advances in Deep Reinforcement learning and computational capabilities of GPUs have led to variety of research being conducted in the learning side of robotics. The main aim being that of making autonomous robots that are capable of learning how to solve a task on their own with minimal requirement for engineering on the planning, vision, or control side. Efforts have been made to learn the manipulation of rigid objects through the help of human demonstrations, specifically in the tasks such as stacking of multiple blocks on top of each other, inserting a pin into a hole, etc. These Deep RL algorithms successfully learn how to complete a task involving the manipulation of rigid objects, but autonomous manipulation of textile objects such as clothes through Deep RL algorithms is still not being studied in the community.
The main objectives of this work involve, 1) implementing the state of the art Deep RL algorithms for rigid object manipulation and getting a deep understanding of the working of these various algorithms, 2) Creating an open-source simulation environment for simulating textile objects such as clothes, 3) Designing Deep RL algorithms for learning autonomous manipulation of textile objects through demonstrations.Peer ReviewedPreprin