1,457 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
Object Handovers: a Review for Robotics
This article surveys the literature on human-robot object handovers. A
handover is a collaborative joint action where an agent, the giver, gives an
object to another agent, the receiver. The physical exchange starts when the
receiver first contacts the object held by the giver and ends when the giver
fully releases the object to the receiver. However, important cognitive and
physical processes begin before the physical exchange, including initiating
implicit agreement with respect to the location and timing of the exchange.
From this perspective, we structure our review into the two main phases
delimited by the aforementioned events: 1) a pre-handover phase, and 2) the
physical exchange. We focus our analysis on the two actors (giver and receiver)
and report the state of the art of robotic givers (robot-to-human handovers)
and the robotic receivers (human-to-robot handovers). We report a comprehensive
list of qualitative and quantitative metrics commonly used to assess the
interaction. While focusing our review on the cognitive level (e.g.,
prediction, perception, motion planning, learning) and the physical level
(e.g., motion, grasping, grip release) of the handover, we briefly discuss also
the concepts of safety, social context, and ergonomics. We compare the
behaviours displayed during human-to-human handovers to the state of the art of
robotic assistants, and identify the major areas of improvement for robotic
assistants to reach performance comparable to human interactions. Finally, we
propose a minimal set of metrics that should be used in order to enable a fair
comparison among the approaches.Comment: Review paper, 19 page
Analysis and Observations from the First Amazon Picking Challenge
This paper presents a overview of the inaugural Amazon Picking Challenge
along with a summary of a survey conducted among the 26 participating teams.
The challenge goal was to design an autonomous robot to pick items from a
warehouse shelf. This task is currently performed by human workers, and there
is hope that robots can someday help increase efficiency and throughput while
lowering cost. We report on a 28-question survey posed to the teams to learn
about each team's background, mechanism design, perception apparatus, planning
and control approach. We identify trends in this data, correlate it with each
team's success in the competition, and discuss observations and lessons learned
based on survey results and the authors' personal experiences during the
challenge
Zero-gravity movement studies
The use of computer graphics to simulate the movement of articulated animals and mechanisms has a number of uses ranging over many fields. Human motion simulation systems can be useful in education, medicine, anatomy, physiology, and dance. In biomechanics, computer displays help to understand and analyze performance. Simulations can be used to help understand the effect of external or internal forces. Similarly, zero-gravity simulation systems should provide a means of designing and exploring the capabilities of hypothetical zero-gravity situations before actually carrying out such actions. The advantage of using a simulation of the motion is that one can experiment with variations of a maneuver before attempting to teach it to an individual. The zero-gravity motion simulation problem can be divided into two broad areas: human movement and behavior in zero-gravity, and simulation of articulated mechanisms
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