RoboCrowd: Scaling Robot Data Collection through Crowdsourcing (2411.01915v2)

Abstract: In recent years, imitation learning from large-scale human demonstrations has emerged as a promising paradigm for training robot policies. However, the burden of collecting large quantities of human demonstrations is significant in terms of collection time and the need for access to expert operators. We introduce a new data collection paradigm, RoboCrowd, which distributes the workload by utilizing crowdsourcing principles and incentive design. RoboCrowd helps enable scalable data collection and facilitates more efficient learning of robot policies. We build RoboCrowd on top of ALOHA (Zhao et al. 2023) -- a bimanual platform that supports data collection via puppeteering -- to explore the design space for crowdsourcing in-person demonstrations in a public environment. We propose three classes of incentive mechanisms to appeal to users' varying sources of motivation for interacting with the system: material rewards, intrinsic interest, and social comparison. We instantiate these incentives through tasks that include physical rewards, engaging or challenging manipulations, as well as gamification elements such as a leaderboard. We conduct a large-scale, two-week field experiment in which the platform is situated in a university cafe. We observe significant engagement with the system -- over 200 individuals independently volunteered to provide a total of over 800 interaction episodes. Our findings validate the proposed incentives as mechanisms for shaping users' data quantity and quality. Further, we demonstrate that the crowdsourced data can serve as useful pre-training data for policies fine-tuned on expert demonstrations -- boosting performance up to 20% compared to when this data is not available. These results suggest the potential for RoboCrowd to reduce the burden of robot data collection by carefully implementing crowdsourcing and incentive design principles.

• The paper introduces RoboCrowd, a framework using crowdsourcing and strategic incentives to efficiently collect large-scale robot demonstration data.
• A field experiment with over 800 interaction episodes showed that material rewards and competitive gamification significantly increased user engagement and data quality.
• The results offer actionable insights for democratizing imitation learning data collection and advancing scalable robotic training methods.

Scaling Robot Data Collection through Crowdsourcing: Insights from RoboCrowd

In the paper titled "RoboCrowd: Scaling Robot Data Collection through Crowdsourcing," the authors introduce a novel approach to collecting large-scale robot demonstration data by integrating crowdsourcing principles with strategic incentive mechanisms. This work addresses a pivotal challenge in the field of imitation learning (IL), which relies on substantial datasets of human demonstrations to train effective robotic policies. Traditional data collection methods often require expert operators and are constrained by significant time and resource investments. The proposed RoboCrowd framework aims to alleviate these constraints by adopting a scalable, public-oriented data collection model.

Key Contributions and Methods

Central to the RoboCrowd framework is a method of distributing the data collection process using a strategic mixture of three incentive mechanisms tailored to appeal to diverse user motivations. These are:

1. Material Rewards: Providing tangible rewards, such as candies, that users can collect through task completion.
2. Intrinsic Interest: Designing tasks that inherently engage or challenge users, thereby motivating interaction based on personal interest or enjoyment.
3. Social Comparison: Incorporating elements of competition, such as leaderboards, to motivate users through social comparison.

These incentives were instantiated in a field experiment utilizing the ALOHA platform, a bimanual puppet-based teleoperation system, placed in a public university setting. A two-week deployment captured data from over 200 volunteer participants, resulting in over 800 interaction episodes. These episodes were analyzed to assess the relationship between incentive mechanisms and user engagement, data volume, and quality.

Experimental Insights

The experimental results provide compelling evidence for the design effectiveness of the proposed incentive mechanisms. Users demonstrated a significant inclination toward tasks with preferred rewards or intriguing challenges over less appealing or simpler ones. For instance, in scenarios where candies served as rewards, tasks associated with more desirable candies received double the user engagement. Moreover, tasks engineered to be more challenging engaged users up to four times longer than simpler counterparts, despite offering the same material reward.

Additionally, users who interacted with the social comparison mechanism, such as the leaderboard, contributed higher quality and quantity of data than those who did not engage in competitive elements. This suggests that gamification can enhance user performance and persistence in task completion, resulting in richer datasets.

Implications for Imitation Learning and Robotics

The implications of this work are multifold. Practically, RoboCrowd presents a feasible method for overcoming the logistical bottlenecks inherent in collecting high-quality robotic training data. By leveraging crowdsourcing in public spaces, researchers can assemble diverse and robust datasets necessary for training effective robot policies.

Theoretically, the research highlights important considerations in human-robot interaction and incentive design within public contexts. It suggests a potential pathway towards democratizing data collection, making it accessible and efficient without sacrificing data integrity or quality.

Looking Forward

This paper opens new avenues for leveraging human social and motivational constructs to optimize the data collection frameworks within robotics. Future developments of RoboCrowd could expand to include varied task types and more sophisticated incentive schemes, potentially incorporating digital rewards or augmented reality elements to further engage users. Additionally, as artificial intelligence systems become integrated into more aspects of daily life, establishing collaborative data ecosystems that involve non-expert contributions may become increasingly valuable.

Overall, RoboCrowd represents an innovative step toward solving a critical issue in robot training and artificial intelligence development, indicating substantial promise for enhancing scalability and efficiency in robot data collection through human-computer interaction principles.