Representing Positional Information in Generative World Models for Object Manipulation (2409.12005v2)

Abstract: Object manipulation capabilities are essential skills that set apart embodied agents engaging with the world, especially in the realm of robotics. The ability to predict outcomes of interactions with objects is paramount in this setting. While model-based control methods have started to be employed for tackling manipulation tasks, they have faced challenges in accurately manipulating objects. As we analyze the causes of this limitation, we identify the cause of underperformance in the way current world models represent crucial positional information, especially about the target's goal specification for object positioning tasks. We introduce a general approach that empowers world model-based agents to effectively solve object-positioning tasks. We propose two declinations of this approach for generative world models: position-conditioned (PCP) and latent-conditioned (LCP) policy learning. In particular, LCP employs object-centric latent representations that explicitly capture object positional information for goal specification. This naturally leads to the emergence of multimodal capabilities, enabling the specification of goals through spatial coordinates or a visual goal. Our methods are rigorously evaluated across several manipulation environments, showing favorable performance compared to current model-based control approaches.

• The paper proposes two novel methods, Position-Conditioned Policy (PCP) and Latent-Conditioned Policy (LCP), to improve the representation of crucial positional information in generative world models for complex object manipulation tasks.
• Experimental evaluation shows that both PCP and LCP significantly enhance baseline model success rates in various manipulation environments, with LCP enabling flexible, multimodal goal representation.
• These advancements in positional representation could substantially improve RL-based manipulation in real-world robotics by enabling better handling of position-sensitive environments and tasks.

Exploring Positional Representation in Generative World Models for Object Manipulation

Recent advancements in reinforcement learning (RL) have escalated the potential of robotic systems to execute complex object manipulation tasks. However, these tasks still present significant challenges when the agents lack sufficient capability to handle positional data within their learning architectures. The paper "Representing Positional Information in Generative World Models for Object Manipulation" investigates how generative world models can be refined to improve performance in such scenarios, specifically focusing on enhancing the representation of positional information.

The authors identify a key limitation in current model-based approaches: the inefficiency in representing crucial positional information, which is vital for object manipulation tasks. This deficiency, particularly in representing target goal specifications for object positioning tasks, stifles the performance of existing solutions like the Dreamer model and its variants. To address this, the paper proposes two approaches: Position-Conditioned Policy (PCP) and Latent-Conditioned Policy (LCP), specifically tailored for generative world models.

Key Innovations

1. Position-Conditioned Policy (PCP):

PCP introduces a straightforward modification where the policy network directly accesses positional coordinates of the target within the world model's latent states. This approach minimizes architectural changes, making it broadly applicable across different world model setups, including the flat-segmented structures of the Dreamer and the object-centric models like FOCUS.

1. Latent-Conditioned Policy (LCP):

Emphasizing an object-centric perspective, LCP utilizes object-specific latent representations. It leverages a latent positional encoder to capture relevant positional information within the world model's latent space. This facilitates multimodal goal specification, allowing the agent to interpret goals expressed as spatial vectors or visual targets.

Experimental Evaluation

The proposed methodologies were rigorously evaluated across multiple established manipulation environments, including DMControl's Reacher, Robosuite's Cube Move, and Metaworld's Shelf Place and Pick-and-Place tasks. The empirical results indicate that both PCP and LCP significantly improve the success rates of the baseline models:

• Performance Improvement: The incorporation of direct position conditioning (PCP) demonstrates substantial improvements even in standard world models like Dreamer. Meanwhile, LCP outperforms in environments requiring fine-grained positional accuracy, particularly benefiting from the multimodal goal representation capabilities.
• Versatility in Goal Representation: Unlike existing methods, which often struggle with non-visual goal specifications, LCP provides an inherent flexibility in representing and integrating positional goals, whether expressed in visual or vector forms.

Implications and Future Directions

The advancements in positional representation proposed in this paper could dramatically enhance the effectiveness of RL-based manipulation in real-world robotics applications, where understanding and manipulating complex, position-sensitive environments are crucial. Furthermore, extending these methodologies to encompass other feature representations, such as object shape or configuration, presents promising research directions. Investigating their applicability across varied modalities, including tactile and auditory sensory inputs, could yield even more robust robotic solutions.

This research contributes to the broader field of intelligent robotics by demonstrating a significant leap in model-based reinforcement learning's capacity to handle complex coordination tasks. Its implications for improving autonomy and adaptability in robotics systems could pave the way for more seamless human-robot collaboration and interaction in diverse real-world settings.