3,619 research outputs found
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
Instrumenting and collecting annotated visual grasping datasets to train
modern machine learning algorithms can be extremely time-consuming and
expensive. An appealing alternative is to use off-the-shelf simulators to
render synthetic data for which ground-truth annotations are generated
automatically. Unfortunately, models trained purely on simulated data often
fail to generalize to the real world. We study how randomized simulated
environments and domain adaptation methods can be extended to train a grasping
system to grasp novel objects from raw monocular RGB images. We extensively
evaluate our approaches with a total of more than 25,000 physical test grasps,
studying a range of simulation conditions and domain adaptation methods,
including a novel extension of pixel-level domain adaptation that we term the
GraspGAN. We show that, by using synthetic data and domain adaptation, we are
able to reduce the number of real-world samples needed to achieve a given level
of performance by up to 50 times, using only randomly generated simulated
objects. We also show that by using only unlabeled real-world data and our
GraspGAN methodology, we obtain real-world grasping performance without any
real-world labels that is similar to that achieved with 939,777 labeled
real-world samples.Comment: 9 pages, 5 figures, 3 table
A Survey of Embodied AI: From Simulators to Research Tasks
There has been an emerging paradigm shift from the era of "internet AI" to
"embodied AI", where AI algorithms and agents no longer learn from datasets of
images, videos or text curated primarily from the internet. Instead, they learn
through interactions with their environments from an egocentric perception
similar to humans. Consequently, there has been substantial growth in the
demand for embodied AI simulators to support various embodied AI research
tasks. This growing interest in embodied AI is beneficial to the greater
pursuit of Artificial General Intelligence (AGI), but there has not been a
contemporary and comprehensive survey of this field. This paper aims to provide
an encyclopedic survey for the field of embodied AI, from its simulators to its
research. By evaluating nine current embodied AI simulators with our proposed
seven features, this paper aims to understand the simulators in their provision
for use in embodied AI research and their limitations. Lastly, this paper
surveys the three main research tasks in embodied AI -- visual exploration,
visual navigation and embodied question answering (QA), covering the
state-of-the-art approaches, evaluation metrics and datasets. Finally, with the
new insights revealed through surveying the field, the paper will provide
suggestions for simulator-for-task selections and recommendations for the
future directions of the field.Comment: Under Review for IEEE TETC
Real Time Animation of Virtual Humans: A Trade-off Between Naturalness and Control
Virtual humans are employed in many interactive applications using 3D virtual environments, including (serious) games. The motion of such virtual humans should look realistic (or ‘natural’) and allow interaction with the surroundings and other (virtual) humans. Current animation techniques differ in the trade-off they offer between motion naturalness and the control that can be exerted over the motion. We show mechanisms to parametrize, combine (on different body parts) and concatenate motions generated by different animation techniques. We discuss several aspects of motion naturalness and show how it can be evaluated. We conclude by showing the promise of combinations of different animation paradigms to enhance both naturalness and control
Traversing the Reality Gap via Simulator Tuning
The large demand for simulated data has made the reality gap a problem on the
forefront of robotics. We propose a method to traverse the gap by tuning
available simulation parameters. Through the optimisation of physics engine
parameters, we show that we are able to narrow the gap between simulated
solutions and a real world dataset, and thus allow more ready transfer of
leaned behaviours between the two. We subsequently gain understanding as to the
importance of specific simulator parameters, which is of broad interest to the
robotic machine learning community. We find that even optimised for different
tasks that different physics engine perform better in certain scenarios and
that friction and maximum actuator velocity are tightly bounded parameters that
greatly impact the transference of simulated solutions.Comment: 8 Pages, Submitted to IROS202
Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids
Real-life control tasks involve matters of various substances---rigid or soft
bodies, liquid, gas---each with distinct physical behaviors. This poses
challenges to traditional rigid-body physics engines. Particle-based simulators
have been developed to model the dynamics of these complex scenes; however,
relying on approximation techniques, their simulation often deviates from
real-world physics, especially in the long term. In this paper, we propose to
learn a particle-based simulator for complex control tasks. Combining learning
with particle-based systems brings in two major benefits: first, the learned
simulator, just like other particle-based systems, acts widely on objects of
different materials; second, the particle-based representation poses strong
inductive bias for learning: particles of the same type have the same dynamics
within. This enables the model to quickly adapt to new environments of unknown
dynamics within a few observations. We demonstrate robots achieving complex
manipulation tasks using the learned simulator, such as manipulating fluids and
deformable foam, with experiments both in simulation and in the real world. Our
study helps lay the foundation for robot learning of dynamic scenes with
particle-based representations.Comment: Accepted to ICLR 2019. Project Page: http://dpi.csail.mit.edu Video:
https://www.youtube.com/watch?v=FrPpP7aW3L
Flightmare: A Flexible Quadrotor Simulator
Currently available quadrotor simulators have a rigid and highly-specialized
structure: either are they really fast, physically accurate, or
photo-realistic. In this work, we propose a paradigm-shift in the development
of simulators: moving the trade-off between accuracy and speed from the
developers to the end-users. We use this design idea to develop a novel modular
quadrotor simulator: Flightmare. Flightmare is composed of two main components:
a configurable rendering engine built on Unity and a flexible physics engine
for dynamics simulation. Those two components are totally decoupled and can run
independently from each other. This makes our simulator extremely fast:
rendering achieves speeds of up to 230 Hz, while physics simulation of up to
200,000 Hz. In addition, Flightmare comes with several desirable features: (i)
a large multi-modal sensor suite, including an interface to extract the 3D
point-cloud of the scene; (ii) an API for reinforcement learning which can
simulate hundreds of quadrotors in parallel; and (iii) an integration with a
virtual-reality headset for interaction with the simulated environment. We
demonstrate the flexibility of Flightmare by using it for two completely
different robotic tasks: learning a sensorimotor control policy for a quadrotor
and path-planning in a complex 3D environment
Neural Categorical Priors for Physics-Based Character Control
Recent advances in learning reusable motion priors have demonstrated their
effectiveness in generating naturalistic behaviors. In this paper, we propose a
new learning framework in this paradigm for controlling physics-based
characters with significantly improved motion quality and diversity over
existing state-of-the-art methods. The proposed method uses reinforcement
learning (RL) to initially track and imitate life-like movements from
unstructured motion clips using the discrete information bottleneck, as adopted
in the Vector Quantized Variational AutoEncoder (VQ-VAE). This structure
compresses the most relevant information from the motion clips into a compact
yet informative latent space, i.e., a discrete space over vector quantized
codes. By sampling codes in the space from a trained categorical prior
distribution, high-quality life-like behaviors can be generated, similar to the
usage of VQ-VAE in computer vision. Although this prior distribution can be
trained with the supervision of the encoder's output, it follows the original
motion clip distribution in the dataset and could lead to imbalanced behaviors
in our setting. To address the issue, we further propose a technique named
prior shifting to adjust the prior distribution using curiosity-driven RL. The
outcome distribution is demonstrated to offer sufficient behavioral diversity
and significantly facilitates upper-level policy learning for downstream tasks.
We conduct comprehensive experiments using humanoid characters on two
challenging downstream tasks, sword-shield striking and two-player boxing game.
Our results demonstrate that the proposed framework is capable of controlling
the character to perform considerably high-quality movements in terms of
behavioral strategies, diversity, and realism. Videos, codes, and data are
available at https://tencent-roboticsx.github.io/NCP/
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