Ag2Manip: Learning Novel Manipulation Skills with Agent-Agnostic Visual and Action Representations
(2404.17521)Abstract
Autonomous robotic systems capable of learning novel manipulation tasks are poised to transform industries from manufacturing to service automation. However, modern methods (e.g., VIP and R3M) still face significant hurdles, notably the domain gap among robotic embodiments and the sparsity of successful task executions within specific action spaces, resulting in misaligned and ambiguous task representations. We introduce Ag2Manip (Agent-Agnostic representations for Manipulation), a framework aimed at surmounting these challenges through two key innovations: a novel agent-agnostic visual representation derived from human manipulation videos, with the specifics of embodiments obscured to enhance generalizability; and an agent-agnostic action representation abstracting a robot's kinematics to a universal agent proxy, emphasizing crucial interactions between end-effector and object. Ag2Manip's empirical validation across simulated benchmarks like FrankaKitchen, ManiSkill, and PartManip shows a 325% increase in performance, achieved without domain-specific demonstrations. Ablation studies underline the essential contributions of the visual and action representations to this success. Extending our evaluations to the real world, Ag2Manip significantly improves imitation learning success rates from 50% to 77.5%, demonstrating its effectiveness and generalizability across both simulated and physical environments.
Overview
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Ag2Manip introduces agent-agnostic visual and action representations, derived from human manipulation videos, enhancing the generality across different robotic systems.
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Empirical evaluations show that Ag2Manip significantly improves performance in manipulation tasks, achieving higher success rates and showing strong adaptability in various environments.
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Future research for Ag2Manip includes increasing training data diversity, integrating with advanced planning algorithms, and exploring applications in other domains like autonomous driving.
Introducing Ag2Manip: A Framework Leveraging Agent-Agnostic Representations for Robotic Manipulation Learning
Overview
This paper introduces a novel framework named Ag2Manip (Ag), designed to address significant challenges in autonomous robotic systems learning novel manipulation tasks. Ag2Manip mitigates issues related to the domain gap between different robotic embodiments and the ambiguity often inherent in tasks executions resulting from sparse data environments. It utilizes agent-agnostic visual and action representations derived from human manipulation videos but with specifics of embodiments obscured to enhance generalizability.
Key Contributions
Ag2Manip makes several pivotal contributions to the field of robotic manipulation:
- Agent-Agnostic Visual Representation: By obscuring agents (humans or robots) in the training videos, the system focuses on the effects of actions rather than specific actors. This advance allows Ag2Manip to generalize across different robotic systems without the biases introduced by human-centric training data.
- Agent-Agnostic Action Representation: Actions are abstracted to a universal proxy agent, simplifying the complex interactions typical in direct robot manipulation. This innovation facilitates easier learning from sparse data through a focus on crucial interactions, like those between the end-effector and objects.
- Empirical Validation: Ag2Manip demonstrates impressive performance improvements in simulated environments. In benchmarks such as FrankaKitchen, ManiSkill, and PartManip, it achieves a 325% increase in performance compared to prior models, all without requiring domain-specific demonstrations.
Empirical Insights and Findings
Several empirical insights emerge from the validation of Ag2Manip:
- Performance Superiority: The use of agent-agnostic visual and action representations significantly enhances manipulation skill acquisition. In practical terms, this translates to a rise in success rates from 50% to 77.5% in real-world imitation learning tasks.
- Generalizability and Adaptation: The framework shows high adaptability across various simulated and physical environments, suggesting its potential utility in diverse real-world applications.
- Challenges and Potential Improvements: While Ag2Manip handles a wide array of tasks effectively, certain tasks involving complex interactions (like button pressing or fine manipulation) still pose challenges. These are partly due to limitations in current training paradigms that could potentially be addressed by integrating more diverse and detailed demonstration data.
Theoretical and Practical Implications
Theoretically, Ag2Manip pushes forward the understanding of how robots can learn manipulation tasks in a domain-agnostic manner. Practically, its ability to learn without task-specific demonstrations or expert input hints at reduced costs and barriers for deploying advanced robotics in various industries, from manufacturing to service automation.
Future Directions
Looking ahead, there are several avenues for further research:
- Enhanced Training Data Diversity: Incorporating a wider variety of tasks and scenarios in the training data could help address current performance limitations.
- Integration with Advanced Planning Algorithms: Combining Ag2Manip's learning capabilities with sophisticated planning algorithms may enhance performance on tasks requiring high precision and adaptability.
- Cross-Domain Applications: Exploring applications beyond robotic manipulation, such as autonomous driving or drone operation, where agent-agnostic learning could generalize skills across various platforms.
Conclusion
Ag2Manip represents a significant step forward in the autonomous learning of robotic manipulation tasks. By abstracting both visual perceptions and actions to be agent-agnostic, it effectively reduces the learning complexity and enhances the generalizability of the learned skills, paving the way for more adaptable and competent robotic systems in the future.