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BULLET REAL-TIME PHYSICS SIMULATION

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ROBOT TABLE TENNIS RESEARCH USING PYBULLET WITH VIRTUAL REALITY


April 17, 2019

admin

This Robot Table Tennis project shows some very exciting research by
Reza Mahjourian. It uses PyBullet and its Virtual Reality physics
server support. Check out
https://sites.google.com/corp/view/robottabletennis and his PhD
thesis, Arxiv paper and video links here:
https://www.cs.utexas.edu/~reza

TOSSINGBOT: LEARNING TO THROW ARBITRARY OBJECTS WITH RESIDUAL PHYSICS


March 30, 2019

admin

TossingBot, a new paper by Google Robotics (Andy Zeng, Shuran Song,
Johnny Lee, Alberto Rodriguez, Thomas Funkhouser
) using PyBullet.

We investigate whether a robot arm can learn to pick and throw
arbitrary objects into selected boxes quickly and accurately. Throwing
has the potential to increase the physical reachability and picking
speed of a robot arm. However, precisely throwing arbitrary objects in
unstructured settings presents many challenges: from acquiring
reliable pre-throw conditions (e.g. initial pose of object in
manipulator) to handling varying object-centric properties (e.g. mass
distribution, friction, shape) and dynamics (e.g. aerodynamics). In
this work, we propose an end-to-end formulation that jointly learns to
infer control parameters for grasping and throwing motion primitives
from visual observations (images of arbitrary objects in a bin)
through trial and error. Within this formulation, we investigate the
synergies between grasping and throwing (i.e., learning grasps that
enable more accurate throws) and between simulation and deep learning
(i.e., using deep networks to predict residuals on top of control
parameters predicted by a physics simulator). The resulting system,
TossingBot, is able to grasp and throw arbitrary objects into boxes
located outside its maximum reach range at 500+ mean picks per hour
(600+ grasps per hour with 85% throwing accuracy); and generalizes to
new objects and target locations.

See https://arxiv.org/abs/1903.11239 and a video here:
https://www.youtube.com/watch?v=f5Zn2Up2RjQ&feature=youtu.be

LEARNING AND ADAPTING QUADRUPED GAITS WITH THE “INTELLIGENT TRIAL &
ERROR” ALGORITHM


March 28, 2019

admin

A new ICRA 2019 paper by INRIA/CNRS (Eloïse Dalin, Pierre Desreumaux,
Jean-Baptiste Mouret) using PyBullet:

See a preprint here https://hal.inria.fr/hal-02084619

WHEN RANDOM SEARCH IS NOT ENOUGH: SAMPLE-EFFICIENT AND NOISE-ROBUST
BLACKBOX OPTIMIZATION OF RL POLICIES


March 7, 2019

admin

A new paper by Google Robotics using PyBullet.

Interest in derivative-free optimization (DFO) and “evolutionary
strategies” (ES) has recently surged in the Reinforcement Learning
(RL) community, with growing evidence that they match state of the art
methods for policy optimization tasks. However, blackbox DFO methods
suffer from high sampling complexity since they require a substantial
number of policy rollouts for reliable updates. They can also be very
sensitive to noise in the rewards, actuators or the dynamics of the
environment. In this paper we propose to replace the standard ES
derivative-free paradigm for RL based on simple reward-weighted
averaged random perturbations for policy updates, that has recently
become a subject of voluminous research, by an algorithm where
gradients of blackbox RL functions are estimated via regularized
regression methods. In particular, we propose to use L1/L2 regularized
regression-based gradient estimation to exploit sparsity and
smoothness, as well as LP decoding techniques for handling adversarial
stochastic and deterministic noise. Our methods can be naturally
aligned with sliding trust region techniques for efficient samples
reuse to further reduce sampling complexity. This is not the case for
standard ES methods requiring independent sampling in each epoch. We
show that our algorithms can be applied in locomotion tasks, where
training is conducted in the presence of substantial noise, e.g. for
learning in sim transferable stable walking behaviors for quadruped
robots or training quadrupeds how to follow a path. We further
demonstrate our methods on several OpenAI Gym Mujoco RL tasks. We
manage to train effective policies even if up to 25% of all
measurements are arbitrarily corrupted, where standard ES methods
produce sub-optimal policies or do not manage to learn at all. Our
empirical results are backed by theoretical guarantees.

See also https://arxiv.org/abs/1903.02993

REALIZING LEARNED QUADRUPED LOCOMOTION BEHAVIORS THROUGH KINEMATIC
MOTION PRIMITIVES


February 27, 2019

admin

A new ICRA 2019 paper using PyBullet:

Abhik Singla, Shounak Bhattacharya and Dhaivat Dholakiya are with the
Robert Bosch Centre for Cyber-Physical Systems, IISc, Bangalore,
India.

Humans and animals are believed to use a very minimal set of
trajectories to perform a wide variety of tasks including walking. Our
main objective in this paper is two fold 1) Obtain an effective tool
to realize these basic motion patterns for quadrupedal walking, called
the kinematic motion primitives (kMPs), via trajectories learned from
deep reinforcement learning (D-RL) and 2) Realize a set of behaviors,
namely trot, walk, gallop and bound from these kinematic motion
primitives in our custom four legged robot, called the `Stoch’. D-RL
is a data driven approach, which has been shown to be very effective
for realizing all kinds of robust locomotion behaviors, both in
simulation and in experiment. On the other hand, kMPs are known to
capture the underlying structure of walking and yield a set of derived
behaviors. We first generate walking gaits from D-RL, which uses
policy gradient based approaches. We then analyze the resulting
walking by using principal component analysis. We observe that the
kMPs extracted from PCA followed a similar pattern irrespective of the
type of gaits generated. Leveraging on this underlying structure, we
then realize walking in Stoch by a straightforward reconstruction of
joint trajectories from kMPs. This type of methodology improves the
transferability of these gaits to real hardware, lowers the
computational overhead on-board, and also avoids multiple training
iterations by generating a set of derived behaviors from a single
learned gait.

See also https://arxiv.org/abs/1810.03842
and a video here:
https://www.youtube.com/watch?v=kiLKSqI4KhE&feature=youtu.be

DEEP LAGRANGIAN NETWORKS: USING PHYSICS AS MODEL PRIOR FOR DEEP
LEARNING


February 24, 2019

admin

A new ICLR 2019 paper using PyBullet:

Michael Lutter, Christian Ritter & Jan Peters, Department of Computer
Science Technische Universität Darmstadt

Deep learning has achieved astonishing results on many tasks with
large amounts of data and generalization within the proximity of
training data. For many important real-world applications, these
requirements are unfeasible and additional prior knowledge on the task
domain is required to overcome the resulting problems. In particular,
learning physics models for model-based control requires robust
extrapolation from fewer samples – often collected online in
real-time – and model errors may lead to drastic damages of the
system.
Directly incorporating physical insight has enabled us to obtain a
novel deep model learning approach that extrapolates well while
requiring fewer samples. As a first example, we propose Deep
Lagrangian Networks (DeLaN) as a deep network structure upon which
Lagrangian Mechanics have been imposed. DeLaN can learn the equations
of motion of a mechanical system (i.e., system dynamics) with a deep
network efficiently while ensuring physical plausibility.
The resulting DeLaN network performs very well at robot tracking
control. The proposed method did not only outperform previous model
learning approaches at learning speed but exhibits substantially
improved and more robust extrapolation to novel trajectories and
learns online in real-time.

See also https://openreview.net/forum?id=BklHpjCqKm

COMPARING TASK SIMPLIFICATIONS TO LEARN CLOSED-LOOP OBJECT PICKING
USING DEEP REINFORCEMENT LEARNING


January 30, 2019

admin

A new paper using PyBullet from ETH Zurich (Michel Breyer, Fadri
Furrer, Tonci Novkovic, Roland Siegwart, and Juan Nieto)

Enabling autonomous robots to interact in unstructured environments
with dynamic objects requires manipulation capabilities that can deal
with clutter, changes, and objects’ variability. This paper presents
a comparison of different reinforcement learning-based approaches for
object picking with a robotic manipulator. We learn closed-loop
policies mapping depth camera inputs to motion commands and compare
different approaches to keep the problem tractable, including reward
shaping, curriculum learning and using a policy pre-trained on a task
with a reduced action set to warm-start the full problem. For
efficient and more flexible data collection, we train in simulation
and transfer the policies to a real robot. We show that using
curriculum learning, policies learned with a sparse reward formulation
can be trained at similar rates as with a shaped reward. These
policies result in success rates comparable to the policy initialized
on the simplified task. We could successfully transfer these policies
to the real robot with only minor modifications of the depth image
filtering. We found that using a heuristic to warm-start the training
was useful to enforce desired behavior, while the policies trained
from scratch using a curriculum learned better to cope with unseen
scenarios where objects are removed.

See also https://arxiv.org/abs/1803.04996 and a video here:
https://www.youtube.com/watch?v=ii16Zejmf-E&feature=youtu.be

PYBULLET AND “SIM-TO-REAL: LEARNING AGILE LOCOMOTION FOR QUADRUPED
ROBOTS”


September 8, 2018

admin

PyBullet is receiving regular updates, you can see the latest version
here: https://pypi.org/project/pybullet
Installation and update is simple:
pip install -U pybullet

Check out the PyBullet Quickstart Guide

and clone the github repository
for more PyBullet examples
and OpenAI Gym environments.

A while ago, Our RSS 2018 paper “Sim-to-Real: Learning Agile
Locomotion For Quadruped Robots” is accepted! (with Jie Tan, Tingnan
Zhang, Erwin Coumans, Atil Iscen, Yunfei Bai, Danijar Hafner, Steven
Bohez, Vincent Vanhoucke).

See also the video and
paper on Arxiv .

Erwin @ twitter

DEEPMIMIC: EXAMPLE-GUIDED DEEP REINFORCEMENT LEARNING OF PHYSICS-BASED
CHARACTER SKILLS


August 30, 2018

admin

An excellent SIGGRAPH 2018 paper using Bullet Physics to simulate
physics based character locomotion, by Xue Bin Peng, Pieter Abbeel,
Sergey Levine, Michiel van de Panne.

See https://xbpeng.github.io/projects/DeepMimic/index.html

Update: there is also an implementation using PyBullet

pip3 install pybullet
python3 -m pybullet_envs.deep_mimic.testrl --arg_file
run_humanoid3d_backflip_args.txt

GIBSON ENV: REAL-WORLD PERCEPTION FOR EMBODIED AGENTS


July 30, 2018

admin

The Gibson project, by Stanford University AI Lab uses PyBullet:

Perception and being active (i.e. having a certain level of motion
freedom) are closely tied. Learning active perception and sensorimotor
control in the physical world is cumbersome as existing algorithms are
too slow to efficiently learn in real-time and robots are fragile and
costly. This has given rise to learning in simulation which
consequently casts a question on transferring to real-world. In this
paper, we study learning perception for active agents in real-world,
propose a virtual environment for this purpose, and demonstrate
complex learned locomotion abilities. The primary characteristics of
the learning environments, which transfer into the trained agents, are
I) being from the real-world and reflecting its semantic complexity,
II) having a mechanism to ensure no need to further domain adaptation
prior to deployment of results in real-world, III) embodiment of the
agent and making it subject to constraints of space and physics.

See also http://gibsonenv.stanford.edu/method/

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HOME OF BULLET AND PYBULLET: PHYSICS SIMULATION FOR GAMES, VISUAL
EFFECTS, ROBOTICS AND REINFORCEMENT LEARNING.

RECENT POSTS

* Robot Table Tennis research using PyBullet with Virtual Reality


* TossingBot: Learning to Throw Arbitrary Objects with Residual
Physics


* Learning and adapting quadruped gaits with the “Intelligent
Trial & Error” algorithm


* When random search is not enough: Sample-Efficient and
Noise-Robust Blackbox Optimization of RL Policies


* Realizing Learned Quadruped Locomotion Behaviors through Kinematic
Motion Primitives



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