Trust the PRoC3S: Solving Long-Horizon Robotics Problems with LLMs and Constraint Satisfaction (2406.05572v2)

Abstract: Recent developments in pretrained LLMs applied to robotics have demonstrated their capacity for sequencing a set of discrete skills to achieve open-ended goals in simple robotic tasks. In this paper, we examine the topic of LLM planning for a set of continuously parameterized skills whose execution must avoid violations of a set of kinematic, geometric, and physical constraints. We prompt the LLM to output code for a function with open parameters, which, together with environmental constraints, can be viewed as a Continuous Constraint Satisfaction Problem (CCSP). This CCSP can be solved through sampling or optimization to find a skill sequence and continuous parameter settings that achieve the goal while avoiding constraint violations. Additionally, we consider cases where the LLM proposes unsatisfiable CCSPs, such as those that are kinematically infeasible, dynamically unstable, or lead to collisions, and re-prompt the LLM to form a new CCSP accordingly. Experiments across three different simulated 3D domains demonstrate that our proposed strategy, PRoC3S, is capable of solving a wide range of complex manipulation tasks with realistic constraints on continuous parameters much more efficiently and effectively than existing baselines.

• The paper introduces PRoC3S, a framework using LLMs and constraint satisfaction (CCSP) to solve long-horizon robotics planning tasks by dynamically generating and refining parameterized skill sequences.
• Experiments across simulated domains (Drawing, Arrange) showed PRoC3S achieved superior efficiency and success rates compared to other methods for constraint-heavy tasks.
• This approach suggests a shift towards more adaptive robotic planning using LLMs, enabling task-transferability across environments without domain-specific knowledge.

Insights into "Trust the PRoC3S: Solving Long-Horizon Robotics Problems with LLMs and Constraint Satisfaction"

"Trust the PRoC3S" investigates the application of Pretrained LLMs to solve complex long-horizon robotics planning tasks characterized by constraints and continuous parameters. Traditional robotic task and motion planning frameworks often rely on predefined symbolic components such as predicates and operators to achieve specific goals. However, they struggle in dynamic environments where constraints and goals constantly evolve. This paper attempts to bridge these limitations by integrating LLMs with constraint satisfaction techniques to output a sequence of parameterized skills that adhere to kinematic, geometric, and physical constraints without requiring manual symbolic specifications.

Methodological Overview

The authors introduce a framework called PRoC3S, where the task of planning for robots is rearticulated as a Continuous Constraint Satisfaction Problem (CCSP). The approach is unorthodox in its incorporation of code generation capabilities of LLMs to dynamically draft and modify robotic plans as programs. These programs, termed LLM Programs (LMPs), are parameterized to reflect environmental variability. The paper proposes a two-phase process:

1. LMP Generation: The LLM is prompted to produce a function that generates a plan-sketch of parameterized skills, with the plan's parameters tied to sampler functions. This phase results in a set of open parameters that require resolution to achieve goals without constraint violations.
2. Constraint Satisfaction and Feedback: The second phase involves solving the CCSP by selecting parameter values through sampling. This configuration should yield sequence and parameter choices that satisfy constraints and execute the tasks without failures. Unsuccessful attempts provide constraints-feedback to refine future program outputs.

Experimental and Numerical Validation

Experiments were conducted to validate PRoC3S across three simulated domains (Drawing, Arrange-Blocks, and Arrange-YCB). Each task required solving planning problems with realistic environmental constraints. The results indicated that PRoC3S demonstrated superior efficiency and success rates in accomplishing constraints-laden tasks compared to other contemporary methodologies without system-level modifications or assumptions. The constraints focused primarily on common challenges in robotics such as kinematics, collision, grasp, and placement, reinforcing the locking-unlocking cycle that environment constraints often induce in real-world robotics applications.

Theoretical and Practical Implications

This integration of LLMs with constraint satisfaction modeling is indicative of a shift towards more adaptive robotic planning solutions. By leveraging LLMs' inherent capacity to process and sequence information contextually, and guiding them with environmental feedback mechanisms, PRoC3S has demonstrated that underspecified and generalized task environments can be managed effectively. It opens avenues for developing LMPs that are potentially transferable across tasks and environments, reducing dependency on domain-specific knowledge for robotic planning.

Future Trajectories in AI and Robotics

While the findings of this research offer an advanced methodology in simplifying robotic task planning in constraint-heavy environments, the potential applications and development extend beyond the immediate context. Future work could explore optimization algorithms integral to the sampling procedure for greater efficiency. Moreover, embedding visual reasoning elements may allow the system to handle a broader range of constraint types without prespecified classifiers.

The paper points towards an evolving paradigm where robotic systems can autonomously contextualize and derive operational strategies in highly variable environments. As LLM capabilities continue to mature, there will be deeper integration into even more complex and less deterministic robotic systems, potentially blurring the lines between task and motion planning on one hand, and heuristic-driven, autonomously evolving methodologies on the other.