DayDreamer: World Models for Physical Robot Learning (2206.14176v1)

Abstract: To solve tasks in complex environments, robots need to learn from experience. Deep reinforcement learning is a common approach to robot learning but requires a large amount of trial and error to learn, limiting its deployment in the physical world. As a consequence, many advances in robot learning rely on simulators. On the other hand, learning inside of simulators fails to capture the complexity of the real world, is prone to simulator inaccuracies, and the resulting behaviors do not adapt to changes in the world. The Dreamer algorithm has recently shown great promise for learning from small amounts of interaction by planning within a learned world model, outperforming pure reinforcement learning in video games. Learning a world model to predict the outcomes of potential actions enables planning in imagination, reducing the amount of trial and error needed in the real environment. However, it is unknown whether Dreamer can facilitate faster learning on physical robots. In this paper, we apply Dreamer to 4 robots to learn online and directly in the real world, without simulators. Dreamer trains a quadruped robot to roll off its back, stand up, and walk from scratch and without resets in only 1 hour. We then push the robot and find that Dreamer adapts within 10 minutes to withstand perturbations or quickly roll over and stand back up. On two different robotic arms, Dreamer learns to pick and place multiple objects directly from camera images and sparse rewards, approaching human performance. On a wheeled robot, Dreamer learns to navigate to a goal position purely from camera images, automatically resolving ambiguity about the robot orientation. Using the same hyperparameters across all experiments, we find that Dreamer is capable of online learning in the real world, establishing a strong baseline. We release our infrastructure for future applications of world models to robot learning.

Overview of "DayDreamer: World Models for Physical Robot Learning"

The paper "DayDreamer: World Models for Physical Robot Learning" presents an exploration into the application of the Dreamer algorithm to physical robots, expanding its utility beyond the domain of video games. This research is situated in the context of robot learning, where deep reinforcement learning (RL) remains a prominent and promising approach, though typically impeded by the extensive interaction requirements in real-world settings.

Key Insights

The authors investigate the efficacy of the Dreamer algorithm, which is known for planning within a learned world model to enable data-efficient learning—a contrast to the traditional deep RL reliance on extensive trial-and-error. This paper is groundbreaking in its ambition to apply Dreamer directly to physical robots without the crutch of simulators, thus venturing into the real world where complexity and unpredictability abound.

Experimental Setup

The authors deployed Dreamer across four distinct robots, demonstrating a breadth of applications:

1. Quadruped Locomotion: Dreamer was tasked with teaching a quadruped robot to roll over, stand, and walk, achieving these tasks in merely one hour from scratch. This includes the rapid adaptation of behaviors within ten additional minutes when the robot was exposed to perturbations.
2. Robotic Arms for Pick and Place Tasks: Two separate robotic arms utilized Dreamer to pick and place objects based on sparse rewards obtainable from camera images. Notably, behaviors approached human-level performance—a remarkable achievement for direct-from-scratch learning in the real-world environment.
3. Wheeled Robot Navigation: Utilizing only camera inputs, a wheeled robot learned to navigate to a target position, doing so effectively without any pre-provided information on its orientation, a challenge that was overcome through Dreamer's learning capabilities.

Results and Contributions

The results demonstrate Dreamer’s impressive efficiency, with consistent learning rates across all robot modalities using uniform hyperparameters. The model's robustness and adaptability without resorting to simulators underscore its potential as a baseline for future real-world robot learning tasks. The infrastructure supporting these efforts has been made publicly accessible, potentially catalyzing further research and development in this domain.

Implications and Future Directions

The practical implications of this research are significant, offering a path towards more autonomous and efficient robot learning that is applicable to real-world environments without requiring labor-intensive simulation setups or domain-specific alterations. Theoretically, the successful application of world models like Dreamer in this context suggests a potent avenue for model-based RL to achieve higher sample efficiency and adaptability, even in complex, dynamic environments.

Future work could explore integrating Dreamer with simulators for hybrid approaches, further expanding on both its robustness and efficiency. Moreover, the extension of such models to more complex tasks with higher dimensional sensory inputs and more nuanced reward structures appears promising.

In conclusion, the "DayDreamer" research offers compelling evidence for the capability and application of learned world models within physical robot learning, marking an advancement in how robotic systems can autonomously acquire and refine skills. The continuation of such research holds the potential to significantly augment the versatility and intelligence of autonomous robotic systems.