schedule Aug 9th 01:45 - 02:30 PM place Grand Ball Room 2 people 141 Interested

Imitation Learning has been the backbone of Robots learning from demonstrator's behavior. Join us to know more about How to train a robot to perform task like acrobatics etc.

Two branches of AI - Deep Learning, and Reinforcement Learning are now responsible for many real-world applications. Machine Translation, Speech Recognition, Object Detection, Robot Control, and Drug Discovery - are some of the numerous examples.

Both approaches are data-hungry - DL requires many examples of each class, and RL needs to play through many episodes to learn a policy. Contrast this to human intelligence. A small child can typically see an image just once, and instantly recognize it in other contexts and environments. We seem to possess an innate model/representation of how the world works, which helps us grasp new concepts and adapt to new situations fast. Humans are excellent one/few shot learners. We are able to learn complex tasks by observing and imitating other humans (eg: cooking, dancing or playing soccer) - despite having a different point of view, sense modalities, body structure, mental facility.

Humans may be very good at picking up novel tasks, but Deep RL agents surpass us in performance. Once a Deep RL has learned a good representation [1], it is easy to surpass human performance in complex tasks like Go[2], Dota 2[3], and Starcraft[4]. We are biologically limited by time, memory and computation (A computer can be made to simulate thousands of plays in a minute).

RL struggles with tasks that have sparse rewards. Take an example of a soccer playing robot - controlled by applying a torque to each one of its joints. The environment rewards you when it scores a goal. If the policy is initialized randomly (we apply a random torque to each joint, every few milliseconds) the probability of the robot scoring a goal is negligible - it won't even be able to learn how to stand up. In tasks requiring long term planning or low-level skills, getting to that initial reward can prove impossible. These situations have the potential to greatly benefit from a demonstration - in this case showing the robot how to walk and kick - and then letting it figure out how to score a goal.

We have an abundance of visual data on humans performing various tasks, in the public domain, in the form of videos from sources like YouTube. In Youtube alone, 400 hours of videos are uploaded every minute, and it is easy to find demonstration videos for any skill imaginable. What if we could harness this by designing agents that could learn how to perform tasks - just by watching a video clip?

Imitation Learning, also known as apprenticeship learning, teaches an agent a sequence of decisions through demonstration, often by a human expert. It has been used in many applications such as teaching drones how to fly[5] and autonomous cars how to drive[6] - It relies on domain engineered features - or extremely precise representations such as mocap [7]. Directly applying imitation learning to learn from videos proves challenging, there is a misalignment of representation between the demonstrations and the agent’s environment. For example: How can a robot sensing its world through a 3d point cloud - learn from a noisy 2d video clip of a soccer player dribbling?

Leveraging recent advances in Reinforcement Learning, Self Supervised Learning and Imitation Learning [8] [9] [10], We present a technical deep dive into an end to end framework which:

1) Has prior knowledge about the world intelligence through Self-Supervised Learning - A relatively new area which seeks to build efficient deep learning representations from unlabelled data but training on a surrogate task. The surrogate task can be rotating an image and predicting the rotation angle or cropping two patches of the image, and predicting their relative tasks - or a combination of several such objectives.

2) Has the ability to align the representation of how it senses the world, with that of the video - allowing it to learn diverse tasks from video clips.

3) Has the ability to reproduce a skill, from only a single demonstration - using applied techniques from imitation learning

[1] https://www.cse.iitb.ac.in/~shivaram/papers/ks_adprl_2011.pdf

[2] https://ai.google/research/pubs/pub44806

[3] https://openai.com/five/

[4] https://deepmind.com/blog/alphastar-mastering-real-time-strategy-game-starcraft-ii/

[5] http://cs231n.stanford.edu/reports/2017/pdfs/614.pdf

[6] https://arxiv.org/pdf/1709.07174.pdf

[7] https://en.wikipedia.org/wiki/Motion_capture

[8] https://arxiv.org/pdf/1704.06888v3.pdf

[9] https://bair.berkeley.edu/blog/2018/06/28/daml/

[10] https://arxiv.org/pdf/1805.11592v2.pdf

 
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Outline/Structure of the Case Study

  • (5 Minutes) Demo and explanation of the problem statement
  • The next 35 minutes will focus on covering the prerequisite of the framework, and then the framework itself:
  • (15 Minutes) A practical introduction to Imitation Learning and Reinforcement learning
  • (10 Minutes) A practical introduction to learning representations
  • (15 Minutes) Technical deep dive into the framework
  • (5 Minutes) Conclusion and Q&A

Learning Outcome

  • Theory and practical know-how on:
  • - Self Supervised Learning
  • - Imitation Learning
  • - Reinforcement Learning
  • Along with code examples

Target Audience

We expect this session to be highly relevant for researchers & practitioners who work with deep learning and/or reinforcement learning and track the latest research trends. It is also relevant for those working in settings with a large about of readily available unlabeled data, but hard to obtain labels. It will also serve as practical introduction to reinforcement learning.

Prerequisites for Attendees

Basic knowledge of machine learning and familiarity with deep learning.

schedule Submitted 4 months ago

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