Manipulation By Feel

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Guiding our fingers while typing, enabling us to nimbly strike a matchstick, and
inserting a key in a keyhole all rely on our sense of touch. It has been
shown
that the sense of touch is
very important for dexterous manipulation in humans. Similarly, for many robotic
manipulation tasks, vision alone may not be
sufficient

often, it may be difficult to resolve subtle details such as the exact position
of an edge, shear forces or surface textures at points of contact, and robotic
arms and fingers can block the line of sight between a camera and its quarry.
Augmenting robots with this crucial sense, however, remains a challenging task.

Our goal is to provide a framework for learning how to perform tactile servoing,
which means precisely relocating an object based on tactile information. To
provide our robot with tactile feedback, we utilize a custom-built tactile
sensor, based on similar principles as the GelSight
sensor
developed at MIT. The sensor is
composed of a deformable, elastomer-based gel, backlit by three colored LEDs,
and provides high-resolution RGB images of contact at the gel surface. Compared
to other sensors, this tactile sensor sensor naturally provides geometric
information in the form of rich visual information from which attributes such as
force can be inferred. Previous work using similar sensors has leveraged the
this kind of tactile sensor on tasks such as learning how to
grasp
, improving
success rates when grasping a variety of objects.

Below is the real time raw sensor output as a marker cap is rolled along the gel surface:


Hardware Setup & Task Definition

For our experiments, we use a modified 3-axis CNC router with a tactile sensor
mounted face-down on the end effector of the machine. The robot moves by
changing the X, Y, and Z position of the sensor relative to its working stage,
driving each axis with a separate stepper motor. Because of the precise control
of these motors, our setup can achieve a resolution of roughly 0.04mm, helpful
for careful movements in fine manipulation tasks.



The robot setup, prepared for the die rolling task is described below. The
tactile sensor is mounted on the end effector at the top left of the image,
facing downwards.

We demonstrate our method through three representative manipulation tasks:

  1. Ball repositioning task: The robot moves a small metal ball bearing to a
    target location on the sensor surface. This task can be difficult because coarse
    control will often apply too much force on the ball bearing, causing it to slip
    and shoot away from the sensor with any movement.

  2. Analog stick deflection task: When playing video games, we often rely solely
    on our sense of touch to manipulate an analog stick on a game controller. This
    task is of particular interest because deflecting the analog stick often
    requires an intentional break and return of contact, creating a partial
    observability situation.

  3. Die rolling task: In this task, the robot rolls a 20-sided die from one face
    to another. In this task the risk of the object slipping out under the sensor is
    even greater, thus making the task the hardest of the three. An advantage of
    this task is that it additionally provides an intuitive success metric – when
    the robot has finished manipulation, is the correct number showing face up?


From left to right: The ball repositioning, analog stick, and die rolling tasks.

Each of these control tasks are specified in terms of goal images directly in
tactile space; that is, the robot aims to manipulate the objects so that they
produce a particular imprint upon the gel surface. These goal tactile images can
be more informative and natural to specify than, say, a 3D-pose specification
for an object or desired force vector.

Deep Tactile Model-Predictive Control

How can we utilize our high-dimensional sensory information to accomplish these
control tasks? All three manipulation tasks can be solved using the same
model-based reinforcement learning algorithm, which we call tactile
model-predictive control (tactile MPC)
, built on top of visual
foresight
. It is
important to note that we can use the same set of hyperparameters for each task,
eliminating manual hyperparameter tuning.



A summary of deep tactile model predictive control.

The tactile MPC algorithm works by training an action-conditioned visual
dynamics or video-prediction model on autonomously collected data. This model
learns from raw sensory data, such as image pixels, and is able to directly make
predictions of future images taking as input future hypothetical actions taken
by the robot and starting tactile images we call context frames. No other
information, such as the absolute position of the end effector, is specified.



Video-prediction model architecture.

In tactile MPC, as shown in the figure above, at test time, a large number of
action sequences, 200 in this case, are sampled and the resulting hypothetical
trajectories are predicted by the model. The trajectory which is predicted to
most closely reach the goal is selected, and the first action in this sequence
is taken in the real world by the robot. To allow for recovery in case of small
errors in the model, trajectories the planning procedure is repeated at every
step.

This control scheme has previously been applied and found success at enabling
robots to lift and rearrange objects, even generalizing to previously unseen
objects. If you’re interested in reading more about this, details are available
in the paper
.

To train the video-prediction model, we need to collect diverse data that will
allow the robot to generalize to tactile states that it has not seen before.
While we could sit at the keyboard and tell the robot how to move for every step
of each trajectory, it would be much nicer if we could give the robot a general
idea of how to collect the data, and allow it to do its thing while we catch up
on homework or sleep. With a few simple reset mechanisms ensuring that things on
the stage do not get out of hand over the course of data collection, we are able
to collect data in a fully self-supervised manner, by collecting trajectories
based on randomized action sequences. During these trajectories, the robot
records tactile images from the sensor as well as the randomized actions it
takes at each step. Each task required about 36 hours, in wall clock time, of
data collection to train the respective predictive model, with no human
supervision necessary.



Randomized data collection for the analog stick task (video sped up).

For each of the three tasks, we present representative examples of plans and
rollouts:




Ball rolling task - The robot rolls the ball along the target trajectory.




Analog stick task - To reach the target goal image, the robot breaks and
re-establishes contact with the object.




Die task - The robot rolls the die from the starting face labeled 20 (as seen in
the prediction frames with red borders, which indicate context frames fed into
the video-prediction model) to the one labeled 2.

As can be seen in these example rollouts, using the same framework and model
settings, tactile MPC is able to perform a variety of manipulation tasks.

What’s Next?

We have shown a touch-based control method, tactile MPC, based on learning
forward predictive models for high resolution tactile sensors, which is able to
reposition objects based on user provided goals. The use of this combination of
algorithms and sensors for control seems promising, and more difficult tasks
may be within reach with the use of combined vision and touch sensing. However,
our control horizon remains relatively short, in the tens of timesteps, which
may not be sufficient for more complex manipulation tasks that we would hope to
achieve in the future. In addition substantial improvements are needed on
methods for specifying goals to enable more complex tasks such as general
purpose object positioning or assembly.


This blog post is based on the following paper which will be presented at
International Conference on Robotics and Automation 2019:

  • Manipulation by Feel: Touch-Based Control with Deep Predictive Models
  • Stephen Tian*, Frederik Ebert*, Dinesh Jayaraman, Mayur Mudigonda, Chelsea Finn, Roberto Calandra, Sergey Levine
  • Paper link, video link

We would like to thank Sergey Levine, Roberto Calandra, Mayur Mudigonda, and
Chelsea Finn for their valuable feedback when preparing this blog post.

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