So You Think You Can Scale Up Autonomous Robot Data Collection? (2411.01813v1)

Abstract: A long-standing goal in robot learning is to develop methods for robots to acquire new skills autonomously. While reinforcement learning (RL) comes with the promise of enabling autonomous data collection, it remains challenging to scale in the real-world partly due to the significant effort required for environment design and instrumentation, including the need for designing reset functions or accurate success detectors. On the other hand, imitation learning (IL) methods require little to no environment design effort, but instead require significant human supervision in the form of collected demonstrations. To address these shortcomings, recent works in autonomous IL start with an initial seed dataset of human demonstrations that an autonomous policy can bootstrap from. While autonomous IL approaches come with the promise of addressing the challenges of autonomous RL as well as pure IL strategies, in this work, we posit that such techniques do not deliver on this promise and are still unable to scale up autonomous data collection in the real world. Through a series of real-world experiments, we demonstrate that these approaches, when scaled up to realistic settings, face much of the same scaling challenges as prior attempts in RL in terms of environment design. Further, we perform a rigorous study of autonomous IL methods across different data scales and 7 simulation and real-world tasks, and demonstrate that while autonomous data collection can modestly improve performance, simply collecting more human data often provides significantly more improvement. Our work suggests a negative result: that scaling up autonomous data collection for learning robot policies for real-world tasks is more challenging and impractical than what is suggested in prior work. We hope these insights about the core challenges of scaling up data collection help inform future efforts in autonomous learning.

Challenges in Scaling Up Autonomous Robot Data Collection

The paper "So You Think You Can Scale Up Autonomous Robot Data Collection?" rigorously evaluates the feasibility of using autonomous data collection methods within robotic learning frameworks, specifically focusing on autonomous Imitation Learning (IL). The research systematically critiques the assumption that autonomous robot data collection can effectively scale and improve robotic policy learning, especially for complex real-world tasks.

The authors explore the landscape of robot learning methods, delineating the spectrum between Reinforcement Learning (RL) and IL. While RL is traditionally shrouded in challenges such as significant environment design requirements including reset mechanisms and success detection, IL shifts the burden towards extensive human demonstration collection. The paper situates autonomous IL as a promising middle-ground technique expected to reduce both the environment design load and human supervision burden.

However, the paper's experimental results paint a far more challenging picture. Across a suite of real-world and simulation tasks, the authors find that scaling autonomous IL continues to encounter notable hurdles similar to those faced by RL, contradicting the notion of autonomous IL being a cost-saving solution. Specifically, the authors address:

• Environment Design Challenges: High costs related to reset mechanisms, state detection, and the assumption of stationary dynamics remain significant. In tasks such as sock folding or oven mitt hanging, robust success detection and reset functions were non-trivial, leading to non-viable scaling for more intricate scenarios.
• Human Supervision Costs vs. Autonomous Collection: Contrary to expectations, the inclusion of autonomous data collection resulted in only marginal performance improvements, averaging around 10%. More so, the paper emphasizes that a focus on collecting additional human demonstrations can unexpectedly yield higher efficiency and better performance compared to extensive autonomous data collection.

The findings propose that scaling autonomous IL for complex, real-world robotic tasks is more demanding and less effective than expected. The environment design effort, coupled with intermittent performance gains, suggests a need for reconsideration of reliance on autonomous data collection in its current form. The paper calls for the development of methods to generalize environment challenges and effectively scale human supervision, potentially employing advanced foundation models for success detection.

Implications of this work extend into both theoretical and practical domains. Theoretically, it provokes a re-evaluation of resource allocation in robotic learning strategies, emphasizing a nuanced balance between human-provided and autonomous data. Practically, the research identifies key bottlenecks like environment design, urging the community to pivot towards developing innovations that holistically mitigate these issues, including improvements in multi-task learning and pre-training paradigms that leverage diverse data sources.

Future directions hinted at in the paper include exploration into how autonomous data collection paradigms could be better tailored or innovated to truly reduce overall labor without sacrificing learning efficacy, possibly by integrating advanced active learning strategies or employing richer, multi-task learning environments conducive to both task execution and reset learning.