CLIPort: What and Where Pathways for Robotic Manipulation (2109.12098v1)

Abstract: How can we imbue robots with the ability to manipulate objects precisely but also to reason about them in terms of abstract concepts? Recent works in manipulation have shown that end-to-end networks can learn dexterous skills that require precise spatial reasoning, but these methods often fail to generalize to new goals or quickly learn transferable concepts across tasks. In parallel, there has been great progress in learning generalizable semantic representations for vision and language by training on large-scale internet data, however these representations lack the spatial understanding necessary for fine-grained manipulation. To this end, we propose a framework that combines the best of both worlds: a two-stream architecture with semantic and spatial pathways for vision-based manipulation. Specifically, we present CLIPort, a language-conditioned imitation-learning agent that combines the broad semantic understanding (what) of CLIP [1] with the spatial precision (where) of Transporter [2]. Our end-to-end framework is capable of solving a variety of language-specified tabletop tasks from packing unseen objects to folding cloths, all without any explicit representations of object poses, instance segmentations, memory, symbolic states, or syntactic structures. Experiments in simulated and real-world settings show that our approach is data efficient in few-shot settings and generalizes effectively to seen and unseen semantic concepts. We even learn one multi-task policy for 10 simulated and 9 real-world tasks that is better or comparable to single-task policies.

• The paper presents a novel two-stream approach that combines CLIP’s semantic understanding with spatial reasoning for precise robotic manipulation.
• The paper introduces language-conditioned policies that enable efficient pick-and-place operations with minimal training examples.
• The paper demonstrates extensive experimental success, showing robust generalization to new objects, colors, and multi-task scenarios in both simulated and real-world settings.

CLIPort: What and Where Pathways for Robotic Manipulation

The paper introduces CLIPort, a framework for language-conditioned robotic manipulation. It addresses the challenge of integrating semantic comprehension with spatial precision, a necessity for manipulating diverse and complex objects. The proposed architecture combines the strengths of CLIP for semantic understanding and Transporter Networks for spatial reasoning.

The CLIPort framework employs a two-stream approach: a semantic stream leveraging CLIP for visual-language grounding, and a spatial stream maintaining precise manipulations. This framework is capable of executing language-specified tabletop tasks without explicit geometric or symbolic object representations.

Technical Contributions

1. Two-Stream Architecture: The architecture consists of semantic and spatial pathways. The semantic stream uses CLIP's pre-trained models, aligning image and language characteristics to enable an understanding of diverse attributes such as color and object categories. The spatial stream applies Transporter Networks' concepts to handle spatial positioning for pick-and-place actions.
2. Language-Conditioned Policies: CLIPort forms policies based not on object recognition, but on predicted affordances conditioned by language instructions. This approach enables data-efficient learning from fewer examples, an essential advantage for generalizing manipulation tasks across different objects and contexts.
3. Empirical Analysis: The work provides extensive experimental validation on both simulated and real-world tasks. Notably, it demonstrates effective multi-task learning, surpassing single-task performance in numerous instances, and highlights the system’s ability to generalize to new colors and objects not encountered during training.

Results

The experiments reveal that CLIPort, with its integrated semantic and spatial architecture, achieves high success rates across numerous tasks. It showcases the ability to quickly adapt and learn new skills from minimal examples, demonstrating strong data efficiency. Furthermore, it effectively generalizes across tasks, which involves unseen objects and attributes, thereby illustrating robust semantic understanding paired with actionable precision.

Implications and Future Directions

The integration of broad semantic understanding with precise spatial control opens new avenues for employing AI in dynamic and less controlled environments, circumventing limitations inherent to traditional object-centric approaches. A future trajectory for this research includes addressing tasks that require more complex reasoning and understanding of the task context, potentially integrating neuro-symbolic methods or advanced attention mechanisms to manage intricate object relationships and enable counting objects or comprehending sequential tasks without human annotations.

The presented work has implications in developing more autonomous, flexible robotic systems capable of adapting to dynamically changing human environments. However, it poses challenges, notably in maintaining accuracy with partial observability and in extending the manipulation capability to 6-DOF or dexterous control. Attention to safety and bias, especially utilizing models like CLIP trained on extensive and diverse datasets, is also critical for trustworthy deployment.

Overall, CLIPort represents a significant contribution to the field of robotic manipulation, merging semantic language understanding with spatial reasoning in a way that holds promise for broad application in automation and AI.