Integrating Vision, Language, and Action in Robot Learning

Current Trends in Robot Learning: Leveraging Vision, Language, and Action

Recent advancements in robot learning have seen a significant shift towards integrating vision, language, and action to enhance the generalization and adaptability of robotic systems. The field is increasingly focused on developing hierarchical models that can generate and filter subgoals, thereby improving the robustness and efficiency of low-level controllers. This approach not only enhances the performance of robotic tasks but also broadens the scope of zero-shot generalization, allowing robots to perform complex tasks in diverse environments with minimal prior training.

Another notable trend is the use of large pre-trained models to automate the generation of task-relevant keypoints, which facilitates more efficient learning and adaptation across varying object configurations and instances. These models are proving to be crucial in enabling robots to generalize effectively from limited demonstrations, reducing the dependency on extensive manual labeling and human intervention.

Additionally, the incorporation of neuro-symbolic predicates and graph learning for numeric planning is advancing the interpretability and sample efficiency of robot learning systems. These methods are enhancing the ability of robots to form task-specific abstractions and solve complex planning tasks with better generalization and interpretability.

Noteworthy papers include:

  • GHIL-Glue: Demonstrates a 25% improvement in hierarchical models using generative subgoals, setting a new state-of-the-art on the CALVIN benchmark.
  • VLMimic: Achieves significant improvements in fine-grained action learning from limited human videos, outperforming baselines in long-horizon tasks.
  • VisualPredicator: Offers better sample complexity and out-of-distribution generalization compared to hierarchical reinforcement learning and vision-language model planning.
  • KALM: Enables robots to generalize across varying object poses and instances with strong real-world performance from minimal demonstrations.
  • Graph Learning for Numeric Planning: Introduces efficient graph kernels that outperform graph neural networks in numeric planning tasks.

Sources

GHIL-Glue: Hierarchical Control with Filtered Subgoal Images

VLMimic: Vision Language Models are Visual Imitation Learner for Fine-grained Actions

VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planning

Keypoint Abstraction using Large Models for Object-Relative Imitation Learning

Graph Learning for Numeric Planning

$\pi_0$: A Vision-Language-Action Flow Model for General Robot Control

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