Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks (1907.13098v1)

Abstract: Contact-rich manipulation tasks in unstructured environments often require both haptic and visual feedback. It is non-trivial to manually design a robot controller that combines these modalities which have very different characteristics. While deep reinforcement learning has shown success in learning control policies for high-dimensional inputs, these algorithms are generally intractable to deploy on real robots due to sample complexity. In this work, we use self-supervision to learn a compact and multimodal representation of our sensory inputs, which can then be used to improve the sample efficiency of our policy learning. Evaluating our method on a peg insertion task, we show that it generalizes over varying geometries, configurations, and clearances, while being robust to external perturbations. We also systematically study different self-supervised learning objectives and representation learning architectures. Results are presented in simulation and on a physical robot.

• The paper proposes a novel variational model that integrates visual, haptic, and proprioceptive inputs for robust sensory representation in robotic manipulation.
• It demonstrates improved performance in a peg insertion task through self-supervised learning that boosts sample efficiency and generalization.
• The study validates the method on both simulated and real robotic platforms, highlighting its transferability and robustness to disturbances.

Multi-Modal Representations in Contact-Rich Robotic Manipulation

The paper "Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks" addresses the challenge of training robots to effectively utilize combined sensory feedback from vision and touch during manipulation tasks. Traditional methods often rely heavily on either handcrafted features or require substantial task-specific knowledge, limiting their scalability and adaptability to new environments. The authors propose a novel approach using machine learning to generate a compact, multimodal representation of sensory data that enhances the efficiency of policy learning in robots.

Key Contributions

1. Variational Model for Multimodal Representation: The authors introduce a new variational method for learning representations that integrates heterogeneous sensory inputs from visual, haptic, and proprioceptive data. This approach leverages self-supervised learning objectives, identifying correlations between sensory modalities to build a robust representation.
2. Contact-Rich Task Evaluation: The methodology is evaluated on a peg insertion task which includes variations in peg geometry, configuration, and clearance. This task was chosen because it naturally integrates both visual and haptic feedback. The robot was required to make and correct contact interactions, highlighting the benefit of using a multi-modal sensor architecture.
3. Comparison with Baseline Models: An in-depth comparison is conducted between various representation learning models, including deterministic and reconstruction-based approaches. The variational model with self-supervised tasks showed superior performance, reinforcing the applicability of the introduced joint sensory approach over traditional methods.
4. Real and Simulated Environments: The proposed representations and policies were assessed both in simulation and on physical robotic platforms, including the Kuka LBR IIWA and Franka Panda robots. Importantly, the paper demonstrated successful transferability of learned policies across different robot architectures and task variations, suggesting strong potential for practical applications in diverse scenarios.

Findings and Implications

• Modality Integration: The empirical results underscore the importance of combining visual and haptic sensory streams. Models that ignored either the visual or haptic data typically underperformed, thereby affirming the hypothesis that multi-modal integration results in a more comprehensive understanding of the task environment.
• Sample Efficiency: By breaking down the representation learning into self-supervised tasks, the authors were able to mitigate the data inefficiency typically associated with deep reinforcement learning methods. This approach could reduce the cost and time of training robots by maximizing information extraction from fewer samples.
• Robustness to Perturbations: The learned models demonstrated robustness to external disturbances, such as occlusions and physical perturbations, maintaining task performance criteria. This robustness is crucial for real-world applications where sensor noise and unexpected environmental changes are prevalent.

Theoretical and Practical Outlook

The authors provide a solid groundwork for future research into multi-modal representation learning in robotics. Their method aligns with the emerging paradigm of utilizing large heterogeneous data streams to enhance robotic autonomy and adaptability. The variational approach combined with self-supervision introduces a novel path in handling complex sensory data that could be extended to include additional sensory inputs such as auditory or thermal feedback. Future developments could also focus on scaling the approach to more complex multi-step tasks in unstructured environments, with implications for fields like autonomous vehicles, service robotics, and advanced manufacturing.

This work invites further exploration into optimizing the balance between representation compactness and the richness necessary for high-dimensional tasks, potentially through adaptive learning architectures or lifelong learning systems that evolve with experience.