Digital Event Horizon
Researchers at MIT's Computer Science and Artificial Intelligence Laboratory have developed a novel method called "Planning for Robots via Code for Continuous Constraint Satisfaction" (PRoC3S), which enables robots to execute complex tasks safely and effectively by leveraging large language models and vision models.
Researchers at MIT's CSAIL developed a novel method called PRoC3S to teach robots to execute complex tasks safely and effectively.PRoC3S uses large language models (LLMs) and vision models to create a simulation of the robot's environment, allowing it to reason about feasible actions for each step of a long-horizon plan.The method addresses the challenge of generating practical multistep plans that take into account physical constraints and limitations of the machine.PRoC3S has been applied to various tasks, including writing individual letters, drawing stars, and sorting blocks in different positions.The potential applications of PRoC3S include enabling home robots to complete intricate chores in dynamic environments.
In a groundbreaking achievement, researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have made significant strides in teaching robots to execute complex tasks safely and effectively. Building upon the principles of long-horizon robotics, which aim to enable robots to perform open-ended tasks with minimal human intervention, the CSAIL team has developed a novel method called "Planning for Robots via Code for Continuous Constraint Satisfaction" (PRoC3S). This innovative approach leverages large language models (LLMs) and vision models to create a simulation of the robot's environment, allowing it to reason about feasible actions for each step of a long-horizon plan.
The PRoC3S method has been designed to address one of the most significant challenges in robotics: generating practical multistep plans that take into account the physical constraints and limitations of the machine. By using an LLM to sketch up an initial plan, the team then tests each step in a simulation, ensuring that it is safe and realistic. If the sequence of actions is infeasible, the language model generates a new plan until it arrives at one that can be executed by the robot.
The CSAIL researchers have applied this method to a range of tasks, including writing individual letters, drawing stars, and sorting and placing blocks in different positions. In these experiments, PRoC3S successfully completed each task more consistently than comparable approaches like "LLM3" and "Code as Policies." The team's findings suggest that by integrating LLMs with constraint satisfaction techniques, robots can be enabled to perform open-ended tasks that were previously thought to be outside their capabilities.
The potential applications of PRoC3S are vast and varied. In the future, the method could enable home robots to complete more intricate chores in dynamic environments like houses, where they may be prompted to do a general chore composed of many steps (like "make me breakfast"). Additionally, PRoC3S could help mobile robots, such as quadrupeds, navigate complex environments and perform tasks that require a high degree of flexibility and adaptability.
According to PhD student Nishanth Kumar, co-lead author of the paper presenting PRoC3S, the synergy between LLMs and classical robotics systems makes open-ended problem-solving possible. "We're creating a simulation on-the-fly of what's around the robot and trying out many possible action plans," he explains. "Vision models help us create a very realistic digital world that enables the robot to reason about feasible actions for each step of a long-horizon plan."
The CSAIL researchers acknowledge that their work builds upon existing foundations in LLMs and classical robotics systems, but they believe that PRoC3S represents a significant breakthrough in long-horizon robotics. By addressing the challenges associated with open-ended task execution, PRoC3S has the potential to revolutionize the field of robotics and enable robots to perform tasks that were previously thought to be impossible.
In an interview with MIT News, Eric Rosen, a researcher at The AI Institute who is not involved in the research, noted that using foundation models like ChatGPT to control robot actions can lead to unsafe or incorrect behaviors due to hallucinations. "PRoC3S tackles this issue by leveraging foundation models for high-level task guidance, while employing AI techniques that explicitly reason about the world to ensure verifiably safe and correct actions," he said.
The CSAIL researchers' work was supported in part by the National Science Foundation, the Air Force Office of Scientific Research, the Office of Naval Research, the Army Research Office, MIT Quest for Intelligence, and The AI Institute. Their findings were presented at the Conference on Robot Learning (CoRL) in Munich, Germany.
Related Information:
https://news.mit.edu/2024/teaching-robot-its-limits-complete-open-ended-tasks-safely-1212
Published: Thu Dec 12 23:01:35 2024 by llama3.2 3B Q4_K_M
