Dream to Manipulate: Compositional World Models Empowering Robot Imitation Learning with Imagination (2412.14957v2)

Abstract: A world model provides an agent with a representation of its environment, enabling it to predict the causal consequences of its actions. Current world models typically cannot directly and explicitly imitate the actual environment in front of a robot, often resulting in unrealistic behaviors and hallucinations that make them unsuitable for real-world robotics applications. To overcome those challenges, we propose to rethink robot world models as learnable digital twins. We introduce DreMa, a new approach for constructing digital twins automatically using learned explicit representations of the real world and its dynamics, bridging the gap between traditional digital twins and world models. DreMa replicates the observed world and its structure by integrating Gaussian Splatting and physics simulators, allowing robots to imagine novel configurations of objects and to predict the future consequences of robot actions thanks to its compositionality. We leverage this capability to generate new data for imitation learning by applying equivariant transformations to a small set of demonstrations. Our evaluations across various settings demonstrate significant improvements in accuracy and robustness by incrementing actions and object distributions, reducing the data needed to learn a policy and improving the generalization of the agents. As a highlight, we show that a real Franka Emika Panda robot, powered by DreMa's imagination, can successfully learn novel physical tasks from just a single example per task variation (one-shot policy learning). Our project page can be found in: https://dreamtomanipulate.github.io/.

• The paper introduces the DreMa world model that generates data via compositional techniques and object-centric Gaussian Splatting to enhance robot imitation learning.
• It leverages equivariant transformations on limited real-world examples to enable one-shot policy learning, achieving +9.1% in simulation and +33.3% on a Franka Emika Panda robot.
• The model explicitly represents real-world dynamics with integrated physics-based simulation, offering a robust framework for advanced robotic manipulation.

Overview of "Dream to Manipulate: Compositional World Models Empowering Robot Imitation Learning with Imagination"

The paper "Dream to Manipulate" introduces a novel approach for building world models applicable to robotic manipulation. The authors propose DreMa, a compositional world model that integrates advanced techniques in real-time photorealism, Gaussian Splatting, and physics-based simulation. DreMa is designed to explicitly represent real-world dynamics, allowing robots to predict the consequences of their actions and generate data for imitation learning.

Key Contributions

1. Compositional Manipulation World Model: DreMa is the first model to provide a grounded approach to robot imagination, facilitating efficient data generation through object-centric Gaussian Splatting and physics simulators.
2. Simulation and Imagination for Data Generation: The model enables the generation of novel demonstrations through equivariant transformations applied to a limited set of real-world examples. This significantly reduces the data required for imitation learning.
3. Empirical Validation: DreMa's ability to improve the generalization of learned policies was demonstrated both in simulation and with a real Franka Emika Panda robot. Notably, DreMa facilitated one-shot policy learning, achieving improvements of +9.1% in simulation and +33.3% in real-world tasks.

Numerical Results and Evaluation

The evaluations in the paper provide strong numerical evidence for DreMa's effectiveness. By leveraging imagination-generated data, the policy accuracy of robots was significantly enhanced. Single-task and multi-task settings both benefited from DreMa, with a mean accuracy improvement of over 9% in simulated environments. These results underscore the utility of integrating compositional world models into robotic systems.

Theoretical and Practical Implications

Theoretically, DreMa challenges the convention of implicit world models by introducing explicit state representations through learnable digital twins powered by Gaussian Splats. This shift could redefine the development of robotic systems by facilitating more robust policy learning through imagined experiences rather than dependence on vast real-world data.

Practically, DreMa showcases potential applications in robotics where rich, interactive environments are essential. The model's ability to handle complex dynamics and predict diverse scenarios makes it a promising tool for improving robotic manipulation in real-world settings.

Discussion on Future Developments

Future research may focus on addressing current limitations, such as the handling of deformable or articulated objects. Integrating advances in dynamic Gaussian Splatting could further enable DreMa to accommodate a broader range of physical interactions. Additionally, improving the accuracy of physical parameter estimation would enhance the robustness of the model when applied to diverse and dynamic environments.

In conclusion, "Dream to Manipulate" presents a significant advancement in the field of robotic automation and learning. By integrating explicit world models into robotic systems, the paper lays the groundwork for enhanced imitation learning capabilities, enabling robots to better perform complex manipulations with minimal real-world data. The implications of this work are vast, potentially transforming how robots are trained and deployed in various industries.