10 research outputs found
Neural probabilistic motor primitives for humanoid control
We focus on the problem of learning a single motor module that can flexibly
express a range of behaviors for the control of high-dimensional physically
simulated humanoids. To do this, we propose a motor architecture that has the
general structure of an inverse model with a latent-variable bottleneck. We
show that it is possible to train this model entirely offline to compress
thousands of expert policies and learn a motor primitive embedding space. The
trained neural probabilistic motor primitive system can perform one-shot
imitation of whole-body humanoid behaviors, robustly mimicking unseen
trajectories. Additionally, we demonstrate that it is also straightforward to
train controllers to reuse the learned motor primitive space to solve tasks,
and the resulting movements are relatively naturalistic. To support the
training of our model, we compare two approaches for offline policy cloning,
including an experience efficient method which we call linear feedback policy
cloning. We encourage readers to view a supplementary video (
https://youtu.be/CaDEf-QcKwA ) summarizing our results.Comment: Accepted as a conference paper at ICLR 201
Towards Compute-Optimal Transfer Learning
The field of transfer learning is undergoing a significant shift with the
introduction of large pretrained models which have demonstrated strong
adaptability to a variety of downstream tasks. However, the high computational
and memory requirements to finetune or use these models can be a hindrance to
their widespread use. In this study, we present a solution to this issue by
proposing a simple yet effective way to trade computational efficiency for
asymptotic performance which we define as the performance a learning algorithm
achieves as compute tends to infinity. Specifically, we argue that zero-shot
structured pruning of pretrained models allows them to increase compute
efficiency with minimal reduction in performance. We evaluate our method on the
Nevis'22 continual learning benchmark that offers a diverse set of transfer
scenarios. Our results show that pruning convolutional filters of pretrained
models can lead to more than 20% performance improvement in low computational
regimes
NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research
We introduce the Never Ending VIsual-classification Stream (NEVIS'22), a
benchmark consisting of a stream of over 100 visual classification tasks,
sorted chronologically and extracted from papers sampled uniformly from
computer vision proceedings spanning the last three decades. The resulting
stream reflects what the research community thought was meaningful at any point
in time. Despite being limited to classification, the resulting stream has a
rich diversity of tasks from OCR, to texture analysis, crowd counting, scene
recognition, and so forth. The diversity is also reflected in the wide range of
dataset sizes, spanning over four orders of magnitude. Overall, NEVIS'22 poses
an unprecedented challenge for current sequential learning approaches due to
the scale and diversity of tasks, yet with a low entry barrier as it is limited
to a single modality and each task is a classical supervised learning problem.
Moreover, we provide a reference implementation including strong baselines and
a simple evaluation protocol to compare methods in terms of their trade-off
between accuracy and compute. We hope that NEVIS'22 can be useful to
researchers working on continual learning, meta-learning, AutoML and more
generally sequential learning, and help these communities join forces towards
more robust and efficient models that efficiently adapt to a never ending
stream of data. Implementations have been made available at
https://github.com/deepmind/dm_nevis
Game Plan: What AI can do for Football, and What Football can do for AI
The rapid progress in artificial intelligence (AI) and machine learning has
opened unprecedented analytics possibilities in various team and individual
sports, including baseball, basketball, and tennis. More recently, AI
techniques have been applied to football, due to a huge increase in data
collection by professional teams, increased computational power, and advances
in machine learning, with the goal of better addressing new scientific
challenges involved in the analysis of both individual players' and coordinated
teams' behaviors. The research challenges associated with predictive and
prescriptive football analytics require new developments and progress at the
intersection of statistical learning, game theory, and computer vision. In this
paper, we provide an overarching perspective highlighting how the combination
of these fields, in particular, forms a unique microcosm for AI research, while
offering mutual benefits for professional teams, spectators, and broadcasters
in the years to come. We illustrate that this duality makes football analytics
a game changer of tremendous value, in terms of not only changing the game of
football itself, but also in terms of what this domain can mean for the field
of AI. We review the state-of-the-art and exemplify the types of analysis
enabled by combining the aforementioned fields, including illustrative examples
of counterfactual analysis using predictive models, and the combination of
game-theoretic analysis of penalty kicks with statistical learning of player
attributes. We conclude by highlighting envisioned downstream impacts,
including possibilities for extensions to other sports (real and virtual)