End-to-End Affordance Learning for Robotic Manipulation (2209.12941v1)

Abstract: Learning to manipulate 3D objects in an interactive environment has been a challenging problem in Reinforcement Learning (RL). In particular, it is hard to train a policy that can generalize over objects with different semantic categories, diverse shape geometry and versatile functionality. Recently, the technique of visual affordance has shown great prospects in providing object-centric information priors with effective actionable semantics. As such, an effective policy can be trained to open a door by knowing how to exert force on the handle. However, to learn the affordance, it often requires human-defined action primitives, which limits the range of applicable tasks. In this study, we take advantage of visual affordance by using the contact information generated during the RL training process to predict contact maps of interest. Such contact prediction process then leads to an end-to-end affordance learning framework that can generalize over different types of manipulation tasks. Surprisingly, the effectiveness of such framework holds even under the multi-stage and the multi-agent scenarios. We tested our method on eight types of manipulation tasks. Results showed that our methods outperform baseline algorithms, including visual-based affordance methods and RL methods, by a large margin on the success rate. The demonstration can be found at https://sites.google.com/view/rlafford/.

Analysis of End-to-End Affordance Learning for Robotic Manipulation

The paper "End-to-End Affordance Learning for Robotic Manipulation" addresses the significant challenge of generalizing a robotic manipulation policy across objects of various semantic categories, shapes, and functions. This is achieved without relying on pre-defined action primitives, which traditionally limit task applicability. The concept of affordance learning is utilized here to enhance Reinforcement Learning (RL) strategies by predicting contact maps derived from the generated contact information during training. This approach significantly distinguishes itself from methodologies that depend heavily on human-demonstrated action primitives, thereby broadening the scope of tasks that can be addressed by a single RL policy.

Technical Contributions and Methods

The proposed method effectively combines visual affordance techniques with RL in an end-to-end training framework. This integration leverages the contact information generated during manipulation to predict affordances, which informs and improves RL policies. Importantly, this system does not rely on two-stage training processes or human interventions typically required in affordance learning. The model independently learns affordances through real-time interaction experiences, thereby simplifying the training pipeline.

The primary contributions are:

• Contact-Based Affordance Prediction: The paper employs contact information to predict affordance maps that facilitate decision-making for manipulation tasks. This contrasts with traditional affordance learning methods requiring human demonstrations and task-specific action planning.
• End-to-End Integration: Affordances are embedded into both the observation space and reward structure of RL policies. This dual utilization allows the RL framework to learn from affordances dynamically, adapting to real-time feedback.
• Multi-Agent and Multi-Stage Capabilities: The proposed affordance learning framework is adept at handling complex scenarios involving multiple agents or several sequential manipulation stages without needing distinct primitive action planning phases.

Results and Implications

Testing on various manipulation tasks, the proposed framework outperformed existing RL and visual affordance methods by significant margins. The multi-stage and multi-agent task scenarios revealed the robustness and flexibility of the affordance-driven approach, indicating its potential for application across diverse and complex real-world tasks.

• Quantitative Results: The experimental results highlighted a strong performance in terms of success rates across multiple manipulation benchmarks, demonstrating the efficacy of contact-driven affordance learning.
• Generalization and Flexibility: Unlike many affordance methods that require extensive pre-training or additional data collection processes, this approach is directly applied to new tasks, emphasizing its generalization capabilities.
• Simulation to Real-World Transfer: Demonstrating promising results in real-world settings further underscores the practical applicability of the trained models.

Future Perspectives

The developments presented in this paper open several avenues for further research and refinement:

• Scalable Learning Frameworks: Exploring mechanisms to expand the framework's ability to handle even more intricate tasks and environmental variations could improve RL systems for highly unstructured or uncertain conditions.
• Enhanced Reward and Observation Integration: Investigating alternative architectures or strategies to better exploit affordance maps within RL could yield further enhancements in task performance and learning rate.
• Cross-Task Transfer Learning: Extending the framework's capabilities to automatically transfer learned affordances across significantly different tasks or object domains poses an exciting future direction.

In summary, this paper contributes to the field by pioneering an integrated solution for affordance learning in robotic manipulation, advancing methodologies that empower more nuanced and generic RL applications without traditional constraints on primitives. This work not only highlights potential pathways for robust robot learning models but also sets a new benchmark for end-to-end affordance learning systems.