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Archive for December, 2018|Monthly archive page

Paper Picks: IROS 2018

In AI, deep learning, Paper Talk, Robotics on December 30, 2018 at 4:18 pm

By Li Yang Ku (Gooly)

I was at IROS in Madrid this October presenting some fan manipulation work I did earlier (see video below), which the King of Spain also attended (see figure above.) When the King is also talking about deep learning, you know what is a hype the trend in robotics. Madrid is a fabulous city, so I am only able to pick a few papers below to share.


a) Roberto Lampariello, Hrishik Mishra, Nassir Oumer, Phillip Schmidt, Marco De Stefano, Alin Albu-Schaffer, “Tracking Control for the Grasping of a Tumbling Satellite with a Free-Floating Robot”

This is work done by folks at DLR (the German Aerospace Center). The goal is to grasp a satellite that is tumbling with another satellite. As you can tell this is a challenging task and this work presents progress extended from a series of previous work done by different space agencies. Research on related grasping tasks can be roughly classified as feedback control methods that solves a regulation control problem and optimal control approaches that computes a feasible optimal trajectory using an open loop approach. In this work, the authors proposes a system that combines both feedback and optimal control. This is achieved by using a motion planner which is generated off-line with all relevant constraints to provide visual servoing a reference trajectory. Servoing will deviate from the original plan but the gross motion will be maintained to avoid motion constraints (such as singularity.) This approach is tested on a gravity free facility. If you haven’t seen one of these zero gravity devices, they are quite common among space agencies and are used to turn off gravity (see figure above.)

b) Josh Tobin, Lukas Biewald , Rocky Duan , Marcin Andrychowicz, Ankur Handa, Vikash Kumar, Bob McGrew, Alex Ray, Jonas Schneider, Peter Welinder, Wojciech Zaremba, Pieter Abbeel, “Domain Randomization and Generative Models for Robotic Grasping.”

This is work done at OpenAI (mostly) that tries to tackle grasping with deep learning. Previous works on grasping with deep learning are usually trained on at most thousands of unique objects, which is relatively small compared to datasets for image classification such as ImageNet. In this work, a new data generation pipeline that cuts meshes and combine them randomly in simulation is proposed. With this approach the authors generated a million unrealistic training data and show that it can be used to learn grasping on realistic objects and achieve similar to state of the art accuracy. The proposed architecture is shown above, α is a convolutional neural network, β is a autoregressive model that generates n different grasps (n=20), and γ is another neural network trained separately to evaluate the grasp using the likelihood of success of the grasp calculated by the autoregressive model plus another observation from the in-hand camera. This use of autoregressive model is an interesting choice where the authors claimed to be advantageous since it can directly compute the likelihood of samples.

c) Barrett Ames, Allison Thackston, George Konidaris, “Learning Symbolic Representations for Planning with Parameterized Skills.”

This is a planning work (by folks I know) that combines parameterized motor skills with higher level planning. At each state the robot needs to select both an action and how to parameterize it. This work introduces a discrete abstract representation for such kind of planning and demonstrated it on Angry Birds and a coffee making task (see figure above.) The authors showed that the approach is capable of generating a state representation that requires very few symbols (here symbols are used to describe preconditions and state estimates), therefore allow an off-the-shelf probabilistic planner to plan faster. Only 16 symbols are needed for the Angry Bird task (not the real Angry Bird, a simpler version) and a plan can be found in 4.5ms. One of the observation is that the only parameter settings needed to be represented by a symbol are the ones that maximizes the probability of reaching the next state on the path to the goal.