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RoboAgent: Generalization and Efficiency in Robot Manipulation via Semantic Augmentations and Action Chunking (2309.01918v1)

Published 5 Sep 2023 in cs.RO and cs.LG

Abstract: The grand aim of having a single robot that can manipulate arbitrary objects in diverse settings is at odds with the paucity of robotics datasets. Acquiring and growing such datasets is strenuous due to manual efforts, operational costs, and safety challenges. A path toward such an universal agent would require a structured framework capable of wide generalization but trained within a reasonable data budget. In this paper, we develop an efficient system (RoboAgent) for training universal agents capable of multi-task manipulation skills using (a) semantic augmentations that can rapidly multiply existing datasets and (b) action representations that can extract performant policies with small yet diverse multi-modal datasets without overfitting. In addition, reliable task conditioning and an expressive policy architecture enable our agent to exhibit a diverse repertoire of skills in novel situations specified using language commands. Using merely 7500 demonstrations, we are able to train a single agent capable of 12 unique skills, and demonstrate its generalization over 38 tasks spread across common daily activities in diverse kitchen scenes. On average, RoboAgent outperforms prior methods by over 40% in unseen situations while being more sample efficient and being amenable to capability improvements and extensions through fine-tuning. Videos at https://robopen.github.io/

Citations (74)

Summary

  • The paper introduces semantic augmentations and action chunking that enhance data diversity and enable up to four-fold improvement in L3 generalization tasks.
  • It presents a multi-task action chunking transformer (MT-ACT) incorporating a CVAE to efficiently manage multi-modal data and produce coherent action sequences.
  • Using only 7,500 teleoperated demonstrations, RoboAgent outperforms contemporary baselines by over 40% across diverse, unseen environments.

Overview of "RoboAgent: Generalization and Efficiency in Robot Manipulation via Semantic Augmentations and Action Chunking"

The paper "RoboAgent: Generalization and Efficiency in Robot Manipulation via Semantic Augmentations and Action Chunking" presents a novel approach to robot manipulation tasks. The document focuses on training a universal robotic agent capable of performing multiple manipulation skills across various environments, leveraging a limited data budget. The introduction of semantic augmentations and action chunking techniques stands as a significant methodological innovation aimed at enhancing the generalization capabilities of robotic systems.

Core Contributions

The paper articulates several key contributions to the field of robotic manipulation:

  1. Semantic Augmentations: The research introduces an automatic method to exponentially augment existing robot manipulation datasets without incurring additional human or robot costs. This method utilizes tools like the SegmentAnything model to apply in-place semantic changes to scenes, thereby training models on a variety of contexts without needing massive data collection efforts.
  2. Multi-Task Action Chunking Transformer (MT-ACT): The authors developed a policy architecture named MT-ACT, particularly adapted for multi-task manipulation environments. It leverages a Conditional Variational Autoencoder (CVAE) to manage the multi-modal distributions of data efficiently and employs action chunking to generate smooth, temporally coherent action sequences over multiple time steps.
  3. Generalization and Efficiency: Using a relatively small dataset of 7,500 teleoperated demonstrations, the RoboAgent demonstrates the ability to generalize to unseen tasks and environments, achieving better performance than contemporary baselines by over 40% in novel scenarios.
  4. Comprehensive Dataset (RoboSet): The paper releases RoboSet, one of the largest open-source robot manipulation datasets utilizing commodity hardware, composed of diverse tasks executed in realistic kitchen setups.

Numerical Results and Evaluation

The evaluation strategy is comprehensive, covering various levels of generalization:

  • L1 Generalization: Variations in object positions and lighting conditions.
  • L2 Generalization: Introduction of new distractor objects and changes in backgrounds.
  • L3 Generalization: Execution of entirely new tasks involving novel skill-object combinations.
  • L4 Generalization: Transferability to entirely new environmental setups, showcasing robustness and adaptability.

The results indicate a pronounced improvement in generalization, notably more significant improvements (up to four-fold) at the L3 level due to semantic augmentations compared to the baseline methods.

Implications and Future Directions

Practically, the research indicates the potential to train robust, generalist robotic systems for real-world applications using minimal datasets bolstered by data augmentation techniques. Theoretically, the multi-task action chunking approach furnishes important insights into handling multi-modal data distributions in robot learning environments.

Looking forward, exploration into combining this framework with reinforcement learning could potentially enhance long-horizon task planning by enabling the composition of learned skills. Moreover, while this paper restricts its generalization analysis to environmental variations, future work could expand to linguistic variation in task descriptions, enriching the comprehension capabilities of robotic systems.

In conclusion, the methodologies proposed in this paper embody promising strides toward achieving efficient training paradigms for adaptable and generalizable robotic systems in diverse and dynamic task environments.