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

• URL: http://arxiv.org/abs/2406.05572v2
• Date: Fri, 6 Sep 2024 02:13:51 GMT
• Title: Trust the PRoC3S: Solving Long-Horizon Robotics Problems with LLMs and Constraint Satisfaction
• Authors: Aidan Curtis, Nishanth Kumar, Jing Cao, Tomás Lozano-Pérez, Leslie Pack Kaelbling,
• Abstract summary: Recent developments in pretrained large language models (LLMs) applied 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. 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.
• Score: 38.683780057806516
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• MALMM: Multi-Agent Large Language Models for Zero-Shot Robotics Manipulation [52.739500459903724]
  Large Language Models (LLMs) have demonstrated remarkable planning abilities across various domains, including robotics manipulation and navigation. We propose a novel multi-agent LLM framework that distributes high-level planning and low-level control code generation across specialized LLM agents. We evaluate our approach on nine RLBench tasks, including long-horizon tasks, and demonstrate its ability to solve robotics manipulation in a zero-shot setting.
  arXiv  Detail & Related papers  (2024-11-26T17:53:44Z)
• Interactive and Expressive Code-Augmented Planning with Large Language Models [62.799579304821826]
  Large Language Models (LLMs) demonstrate strong abilities in common-sense reasoning and interactive decision-making. Recent techniques have sought to structure LLM outputs using control flow and other code-adjacent techniques to improve planning performance. We propose REPL-Plan, an LLM planning approach that is fully code-expressive and dynamic.
  arXiv  Detail & Related papers  (2024-11-21T04:23:17Z)
• DeepLTL: Learning to Efficiently Satisfy Complex LTL Specifications [59.01527054553122]
  Linear temporal logic (LTL) has recently been adopted as a powerful formalism for specifying complex, temporally extended tasks in reinforcement learning (RL) Existing approaches suffer from several shortcomings: they are often only applicable to finite-horizon fragments, are restricted to suboptimal solutions, and do not adequately handle safety constraints. In this work, we propose a novel learning approach to address these concerns. Our method leverages the structure of B"uchia, which explicitly represent the semantics of automat- specifications, to learn policies conditioned on sequences of truth assignments that lead to satisfying the desired formulae.
  arXiv  Detail & Related papers  (2024-10-06T21:30:38Z)
• Directed Exploration in Reinforcement Learning from Linear Temporal Logic [59.707408697394534]
  Linear temporal logic (LTL) is a powerful language for task specification in reinforcement learning. We show that the synthesized reward signal remains fundamentally sparse, making exploration challenging. We show how better exploration can be achieved by further leveraging the specification and casting its corresponding Limit Deterministic B"uchi Automaton (LDBA) as a Markov reward process.
  arXiv  Detail & Related papers  (2024-08-18T14:25:44Z)
• Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? [54.667202878390526]
  Long-context language models (LCLMs) have the potential to revolutionize our approach to tasks traditionally reliant on external tools like retrieval systems or databases. We introduce LOFT, a benchmark of real-world tasks requiring context up to millions of tokens designed to evaluate LCLMs' performance on in-context retrieval and reasoning. Our findings reveal LCLMs' surprising ability to rival state-of-the-art retrieval and RAG systems, despite never having been explicitly trained for these tasks.
  arXiv  Detail & Related papers  (2024-06-19T00:28:58Z)
• LTL-Constrained Policy Optimization with Cycle Experience Replay [19.43224037705577]
  Linear Logic (LTL) offers a precise means for constraining the behavior of reinforcement learning agents. We present Cycle Experience (CyclER), a reward-shaping approach to this problem that allows continuous state and action spaces.
  arXiv  Detail & Related papers  (2024-04-17T17:24:44Z)
• Empowering Large Language Models on Robotic Manipulation with Affordance Prompting [23.318449345424725]
  Large language models fail to interact with the physical world by generating control sequences properly. Existing LLM-based approaches circumvent this problem by relying on additional pre-defined skills or pre-trained sub-policies. We propose a framework called LLM+A(ffordance) where the LLM serves as both the sub-task planner and the motion controller.
  arXiv  Detail & Related papers  (2024-04-17T03:06:32Z)
• Policy Optimization with Linear Temporal Logic Constraints [37.27882290236194]
  We study the problem of policy optimization with linear temporal logic constraints. We develop a model-based approach that enjoys a sample complexity analysis for guaranteeing both task satisfaction and cost optimality.
  arXiv  Detail & Related papers  (2022-06-20T02:58:02Z)
• Reinforcement Learning Based Temporal Logic Control with Soft Constraints Using Limit-deterministic Generalized Buchi Automata [0.0]
  We study the control synthesis of motion planning subject to uncertainties. The uncertainties are considered in robot motion and environment properties, giving rise to the probabilistic labeled Markov decision process (MDP)
  arXiv  Detail & Related papers  (2021-01-25T18:09:11Z)
• Teaching the Old Dog New Tricks: Supervised Learning with Constraints [18.88930622054883]
  Adding constraint support in Machine Learning has the potential to address outstanding issues in data-driven AI systems. Existing approaches typically apply constrained optimization techniques to ML training, enforce constraint satisfaction by adjusting the model design, or use constraints to correct the output. Here, we investigate a different, complementary, strategy based on "teaching" constraint satisfaction to a supervised ML method via the direct use of a state-of-the-art constraint solver.
  arXiv  Detail & Related papers  (2020-02-25T09:47:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.