88 research outputs found
Sim-to-Real Learning of Robust Compliant Bipedal Locomotion on Torque Sensor-Less Gear-Driven Humanoid
In deep reinforcement learning, sim-to-real is the mainstream method as it
needs a large number of trials, however, it is challenging to transfer trained
policy due to reality gap. In particular, it is known that the characteristics
of actuators in leg robots have a considerable influence on the reality gap,
and this is also noticeable in high reduction ratio gears. Therefore, we
propose a new simulation model of high reduction ratio gears to reduce the
reality gap. The instability of the bipedal locomotion causes the sim-to-real
transfer to fail catastrophically, making system identification of the physical
parameters of the simulation difficult. Thus, we also propose a system
identification method that utilizes the failure experience. The realistic
simulations obtained by these improvements allow the robot to perform compliant
bipedal locomotion by reinforcement learning. The effectiveness of the method
is verified using a actual biped robot, ROBOTIS-OP3, and the sim-to-real
transferred policy archived to stabilize the robot under severe disturbances
and walk on uneven terrain without force and torque sensors.Comment: 8 pages. An accompanying video is available at the following link:
https://youtu.be/fZWQq9yAYe
Interactively Picking Real-World Objects with Unconstrained Spoken Language Instructions
Comprehension of spoken natural language is an essential component for robots
to communicate with human effectively. However, handling unconstrained spoken
instructions is challenging due to (1) complex structures including a wide
variety of expressions used in spoken language and (2) inherent ambiguity in
interpretation of human instructions. In this paper, we propose the first
comprehensive system that can handle unconstrained spoken language and is able
to effectively resolve ambiguity in spoken instructions. Specifically, we
integrate deep-learning-based object detection together with natural language
processing technologies to handle unconstrained spoken instructions, and
propose a method for robots to resolve instruction ambiguity through dialogue.
Through our experiments on both a simulated environment as well as a physical
industrial robot arm, we demonstrate the ability of our system to understand
natural instructions from human operators effectively, and how higher success
rates of the object picking task can be achieved through an interactive
clarification process.Comment: 9 pages. International Conference on Robotics and Automation (ICRA)
2018. Accompanying videos are available at the following links:
https://youtu.be/_Uyv1XIUqhk (the system submitted to ICRA-2018) and
http://youtu.be/DGJazkyw0Ws (with improvements after ICRA-2018 submission
Two-fingered Hand with Gear-type Synchronization Mechanism with Magnet for Improved Small and Offset Objects Grasping: F2 Hand
A problem that plagues robotic grasping is the misalignment of the object and
gripper due to difficulties in precise localization, actuation, etc.
Under-actuated robotic hands with compliant mechanisms are used to adapt and
compensate for these inaccuracies. However, these mechanisms come at the cost
of controllability and coordination. For instance, adaptive functions that let
the fingers of a two-fingered gripper adapt independently may affect the
coordination necessary for grasping small objects. In this work, we develop a
two-fingered robotic hand capable of grasping objects that are offset from the
gripper's center, while still having the requisite coordination for grasping
small objects via a novel gear-type synchronization mechanism with a magnet.
This gear synchronization mechanism allows the adaptive finger's tips to be
aligned enabling it to grasp objects as small as toothpicks and washers. The
magnetic component allows this coordination to automatically turn off when
needed, allowing for the grasping of objects that are offset/misaligned from
the gripper. This equips the hand with the capability of grasping light,
fragile objects (strawberries, creampuffs, etc) to heavy frying pan lids, all
while maintaining their position and posture which is vital in numerous
applications that require precise positioning or careful manipulation.Comment: 8 pages. Accepted at IEEE IROS 2023. An accompanying video is
available at https://www.youtube.com/watch?v=RAO7Qb2ZGN
Laboratory Automation: Precision Insertion with Adaptive Fingers utilizing Contact through Sliding with Tactile-based Pose Estimation
Micro well-plates are commonly used apparatus in chemical and biological
experiments that are a few centimeters in thickness with wells in them. The
task we aim to solve is to place (insert) them onto a well-plate holder with
grooves a few millimeters in height. Our insertion task has the following
facets: 1) There is uncertainty in the detection of the position and pose of
the well-plate and well-plate holder, 2) the accuracy required is in the order
of millimeter to sub-millimeter, 3) the well-plate holder is not fastened, and
moves with external force, 4) the groove is shallow, and 5) the width of the
groove is small. Addressing these challenges, we developed a) an adaptive
finger gripper with accurate detection of finger position (for (1)), b) grasped
object pose estimation using tactile sensors (for (1)), c) a method to insert
the well-plate into the target holder by sliding the well-plate while
maintaining contact with the edge of the holder (for (2-4)), and d) estimating
the orientation of the edge and aligning the well-plate so that the holder does
not move when maintaining contact with the edge (for (5)). We show a
significantly high success rate on the insertion task of the well-plate, even
though under added noise.
An accompanying video is available at the following link:
https://drive.google.com/file/d/1UxyJ3XIxqXPnHcpfw-PYs5T5oYQxoc6i/view?usp=sharingComment: 7 pages, 5 figure
Relationship between Salivary Oxytocin Levels and Generosity in Preschoolers
This study examined the association between salivary oxytocin (sOT) levels and generosity in preschoolers. Fifty preschoolers played two dictator games (DG) by deciding how to allocate 10 chocolates between themselves and another child, who was either from the same class as the participant (ingroup member), or an unknown child from another class (outgroup member). sOT levels were assessed in saliva collected from the children immediately prior to the DG tasks. While sOT levels were negatively associated with allocations made to both ingroup and outgroup members by boys, among girl sOT levels were positively related to allocations made to ingroup members, and unrelated to allocations made to outgroup members. These results suggest sex differences in the association between salivary oxytocin and generosity
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