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Toward Open-ended Embodied Tasks Solving (2312.05822v1)

Published 10 Dec 2023 in cs.AI

Abstract: Empowering embodied agents, such as robots, with AI has become increasingly important in recent years. A major challenge is task open-endedness. In practice, robots often need to perform tasks with novel goals that are multifaceted, dynamic, lack a definitive "end-state", and were not encountered during training. To tackle this problem, this paper introduces \textit{Diffusion for Open-ended Goals} (DOG), a novel framework designed to enable embodied AI to plan and act flexibly and dynamically for open-ended task goals. DOG synergizes the generative prowess of diffusion models with state-of-the-art, training-free guidance techniques to adaptively perform online planning and control. Our evaluations demonstrate that DOG can handle various kinds of novel task goals not seen during training, in both maze navigation and robot control problems. Our work sheds light on enhancing embodied AI's adaptability and competency in tackling open-ended goals.

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Citations (3)
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Summary

  • The paper introduces the DOG framework to enhance embodied AI adaptability using diffusion models and energy functions.
  • It employs a two-phase approach where diffusion models learn offline world knowledge and then enable planning for novel, open-ended tasks.
  • DOG demonstrates versatility in tasks like maze navigation and robotic manipulation, highlighting its practical adaptability.

Introduction to Embodied AI and Open-Ended Challenges

Embodied AI has made significant strides in recent years, aiming to empower robots and similar agents with intelligent capabilities. Traditional AI focuses on specific, constrained settings, while embodied AI exists within the physical field, performing a range of tasks like humans and animals. However, the ability to tackle open-ended tasks with dynamic and varied goals is a primary challenge for these systems.

Real-world tasks often come with open-ended goals that are diverse and complex, making them difficult to capture in training completely. To address these challenges, a novel framework called Diffusion for Open-ended Goals (DOG) is introduced. It aims to enhance the adaptability of embodied AI, enabling them to take on tasks with novel goals unseen during their training phase.

Foundations of the Proposed Framework

DOG leverages diffusion models and state-of-the-art techniques for energy-based model guidance, avoiding the need for goal-specific training. The framework undergoes a two-phase approach:

  1. Training Phase: Diffusion models learn world knowledge from offline experiences without goal-conditioning. This phase involves learning the data distribution to predict future states based on the current state.
  2. Testing Phase: When presented with a novel task, the agent refers to its internalized world knowledge to plan and act in line with the goal. The framework uses principles of energy minimization for this purpose, generating plans and actions that strive for the open-ended goal.

The framework is evaluated in different scenarios, ranging from maze navigation to robot arm manipulation, demonstrating its effectiveness in handling diverse goals outside of its training context.

Methodology and Innovations

At its core, DOG is built upon the synergy between the generative abilities of diffusion models and the adaptability of training-free guidance. The key innovations of the framework include:

  • Novel Modeling Scheme: A new formulation infuses the concept of energy functions into Markov decision processes, enhancing the flexibility of decision-making methods.
  • Training-Free Planning: In the inference stage, the agent employs the diffusion model's world knowledge and adapts its actions to minimize the goal energy function, even for goals not present during training.
  • Versatile Execution: A variety of actors can be integrated into the system to enact the planned state transitions, making the framework versatile and applicable across different embodied tasks.

Practical Applications and Performance

The DOG framework shines in various practical tests, notably:

  • Maze Navigation: It exemplary handles tasks such as navigating to certain locations, avoiding areas, and transferring knowledge to new environments.
  • Robot Movement Control: DOG adapts robotic movements to fulfill goals like altering speed and maintaining specific heights, showcasing its application in nuanced control tasks.
  • Robotic Task Execution: Even for complex tasks like manipulating objects, the agents can generate and execute plans for varying goal states, anchored by the framework's generative capabilities.

Conclusion and Future Perspectives

The framework posits a significant advancement in enhancing the competency of embodied AI to tackle open-ended tasks. Although limitations exist, such as the dependence on human-defined energy functions and the need for diverse offline training data, DOG establishes a foundation for future research. It holds promise not only in practical implementations, such as assistive technologies, but also potentially contributes to cognitive studies in understanding human-like intelligence and problem-solving capabilities.

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