Recent developments in pretrained large language models (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.

通过预训练的大型语言模型（LLM）在机器人学中的应用，探讨了 LLM 规划连续参数化技能集合的能力，以避免违反一系列运动学、几何学和物理学约束条件，通过采样或优化来解决连续约束满足问题，并通过实验验证了提出的 PRoC3S 策略在解决复杂操作任务时比现有基线方法更高效和更有效。

信赖 PRoC3S：使用 LLMs 和约束满足解决长时程机器人问题

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

Large Language Models (LLMs) have been shown to be capable of performing
high-level planning for long-horizon robotics tasks, yet existing methods
require access to a pre-defined skill library (e.g. picking, placing, pulling,
pushing, navigating). However, LLM planning does not address how to design or
learn those behaviors, which remains challenging particularly in long-horizon
settings. Furthermore, for many tasks of interest, the robot needs to be able
to adjust its behavior in a fine-grained manner, requiring the agent to be
capable of modifying low-level control actions. Can we instead use the
internet-scale knowledge from LLMs for high-level policies, guiding
reinforcement learning (RL) policies to efficiently solve robotic control tasks
online without requiring a pre-determined set of skills? In this paper, we
propose Plan-Seq-Learn (PSL): a modular approach that uses motion planning to
bridge the gap between abstract language and learned low-level control for
solving long-horizon robotics tasks from scratch. We demonstrate that PSL
achieves state-of-the-art results on over 25 challenging robotics tasks with up
to 10 stages. PSL solves long-horizon tasks from raw visual input spanning four
benchmarks at success rates of over 85%, out-performing language-based,
classical, and end-to-end approaches. Video results and code at
this https URL

利用大型语言模型（LLMs）和运动规划，提出了一种模块化方法 Plan-Seq-Learn（PSL），将抽象语言和学习到的低级控制相结合，从头开始解决长期目标的机器人任务，并在超过 25 个具有挑战性的机器人任务中取得了最先进的结果。