TL;DR:
- Engineers at Princeton University and Google collaborate to teach robots when to seek help and recognize their limitations.
- The technique quantifies ambiguity in human language and prompts robots to ask for clarification.
- Large language models (LLMs), like ChatGPT, are used to assess uncertainty in complex tasks.
- The method allows users to set a desired success level, minimizing reliance on external assistance.
- Extensive testing on robotic arms in real-world scenarios demonstrates high accuracy and reduced help requests.
- Conformal prediction is key in quantifying LLM’s uncertainty and improving overall success rates.
- The research highlights the importance of acknowledging physical limitations in robotics.
- Collaboration between academia and industry continues to drive innovation in robotics and AI.
Main AI News:
In the realm of modern robotics, the ability to perceive and respond to the environment is undeniably impressive. Yet, a crucial aspect often overlooked is the recognition of what robots don’t know. Teaching robots to seek assistance when needed is paramount for enhancing their safety and efficiency.
A groundbreaking collaboration between engineers at Princeton University and Google has yielded an innovative approach to imbuing robots with an awareness of their limitations. This ingenious technique revolves around quantifying the inherent ambiguity in human language and leveraging this measurement to prompt robots to request further guidance. Consider instructing a robot to retrieve a single bowl from a table – a straightforward directive. However, instructing the same robot to select a bowl from a table adorned with five identical bowls introduces a significant level of uncertainty. In such cases, the robot is designed to seek clarification proactively.
Given that tasks for robots often involve complex scenarios beyond simple commands like “pick up a bowl,” engineers turn to large language models (LLMs) for assessing uncertainty in intricate environments. LLMs, akin to the technology underpinning tools like ChatGPT, empower robots to comprehend and interpret human language. However, it’s worth noting that LLM outputs can be prone to unreliability, as highlighted by Anirudha Majumdar, an assistant professor of mechanical and aerospace engineering at Princeton and the senior author of this pioneering study.
“Blindly following plans generated by an LLM could cause robots to act in an unsafe or untrustworthy manner, and so we need our LLM-based robots to know when they don’t know,” emphasizes Majumdar.
What sets this approach apart is its adaptability. It allows a robot’s user to define a specific degree of success, tied to a predefined uncertainty threshold that prompts the robot to seek assistance. For example, a surgical robot may operate with a much lower error tolerance than a robot engaged in household cleaning.
“We want the robot to request help in a manner that aligns with the user’s desired level of success. Simultaneously, we aim to minimize the overall reliance on external assistance,” explains Allen Ren, a graduate student in mechanical and aerospace engineering at Princeton and the lead author of this groundbreaking study. Ren’s outstanding work was recently recognized with a best student paper award, presented at the Conference on Robot Learning in Atlanta.
The results speak volumes about the effectiveness of this method. In comparison to other approaches, it delivers exceptional accuracy while reducing the frequency of help requests by robots. This innovative research was rigorously tested on a simulated robotic arm as well as two types of robots within Google’s facilities in New York City and Mountain View, California.
One particularly intricate experiment involved a robotic arm mounted on a mobile platform situated in an office kitchen, complete with a microwave and a set of recycling, compost, and trash bins. Here, the robot faced the challenge of disambiguating instructions in a real-world setting. In response to the command, “place the bowl in the microwave,” the robot’s LLM-based planner generated multiple possible actions, akin to multiple-choice answers. Each option was assigned a probability, and the researchers employed a statistical method called conformal prediction, coupled with a user-specified guaranteed success rate, to trigger a request for human assistance when probabilities met a specific threshold. This approach ensures that the robot seeks help only when necessary, maintaining a balance between autonomy and guidance.
In another instance, a user instructed the robot to dispose of an apple and a dirty sponge, describing the apple as rotten. Here, the robot’s decision-making process did not trigger a request for clarification, as the action “put the apple in the compost” exhibited a significantly higher probability of correctness than any other alternative.
Anirudha Majumdar emphasizes the significance of conformal prediction in quantifying the language model’s uncertainty, asserting that it elevates the overall success rate while minimizing the need for intervention.
This research underscores the importance of acknowledging physical constraints in robotics; as Andy Zeng, a research scientist at Google DeepMind and a coauthor of the study, aptly points out, “Large language models might talk their way out of a conversation, but they can’t skip gravity.” Collaborative efforts between Ren, Majumdar, and Zeng emerged from the Princeton Robotics Seminar series, illustrating the power of interdisciplinary collaboration.
Ren is now expanding the horizons of this research, delving into the realm of active perception for robots. This entails using predictions to enable a robot to locate objects within a house while being in a different part of the same house. Such endeavors bring forth a new set of challenges in estimating uncertainty and determining when to seek assistance, pushing the boundaries of robotic capabilities further.
Conclusion:
This groundbreaking research not only elevates the intelligence of robots but also underscores the importance of awareness of limitations in the quest for creating genuinely intelligent machines. The collaboration between academia and industry, exemplified by Princeton University and Google, continues to drive innovation at the intersection of robotics and artificial intelligence.
