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Intro

Sequential Decision Making in Complex Environments

Data from the Existing Simulation Environments

Procedural Generation of New Environments

Benchmarking RL Generalization

Benchmarking Safe Reinforcement Learning

Benchmarking Multi-Agent Reinforcement Learning

Real2Sim: Learning to generate traffic scenarios

Pretraining Policy Representation with Real World Data

Self-supervised Learning through Contrastive Learning

Policy Pretraining with Human Actions

Action-conditioned Contrastive Learning

Pretrained Representation for Imitation Learning

Human-in-the-loop Reinforcement Learning

Human-Al Copilot Optimization (HACO)

Demo Video: Learning to drive in CARLA environment

Policy Dissection through Frequency Analysis

Description:

Explore recent advancements in machine autonomy and generalizable embodied AI through this remote talk given at Stanford Graphics Group. Delve into topics such as sequential decision making in complex environments, procedural generation of new environments, and benchmarking reinforcement learning generalization. Learn about innovative approaches like Real2Sim for generating traffic scenarios, policy pretraining with real-world data, and self-supervised learning through contrastive methods. Discover the potential of human-in-the-loop reinforcement learning and human-AI copilot optimization. Gain insights into policy dissection through frequency analysis and witness a demo of learning to drive in the CARLA environment.

Toward Generalizable Embodied AI for Machine Autonomy

Bolei Zhou

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#Computer Science #Artificial Intelligence #Machine Learning #Reinforcement Learning #Programming #Game Development #Procedural Generation #Imitation Learning #Multi-Agent Reinforcement Learning #Contrastive Learning #Embodied AI #Sequential Decision Making