A Few Words Go a Long Way: Language Guided Robot Policy Synthesis

• URL: http://arxiv.org/abs/2607.23784v1
• Date: Sun, 26 Jul 2026 18:00:02 GMT
• Title: A Few Words Go a Long Way: Language Guided Robot Policy Synthesis
• Abstract summary: ARCHITECT is a framework that treats robot policy acquisition as an interactive program synthesis task.<n>We introduce an iterative process where a human supervisor provides natural language corrections to steer the policy.<n>In a benchmark evaluation on a Franka Panda robot, ARCHITECT outperforms state-of-the-art VLA models.
• Score: 9.088404024682706
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While vision-language-action models have demonstrated impressive zero-shot manipulation capabilities, they remain fundamentally black box policies that are difficult to interpret, adapt, or correct when they inevitably fail. In this work, we propose ARCHITECT, a framework that treats robot policy acquisition as an interactive program synthesis task. ARCHITECT leverages the reasoning capabilities of LLM coding agents to synthesize modular robot programs that utilize a suite of perception and control tools. Unlike end-to-end models where distribution shift leads to unpredictable, cascading failures, our modular architecture allows users to isolate failures and localize feedback at the level of abstraction required. We introduce an iterative process where a human supervisor provides natural language corrections to steer the policy. These corrections are grounded in the policy code by program execution traces and distilled into a persistent skill library, a form of long-term in-context learning which enables the agent to accumulate a repertoire of reusable, interpretable behaviors. In a benchmark evaluation on a Franka Panda robot, ARCHITECT outperforms state-of-the-art VLA models and program synthesis baselines on complex, long-horizon tasks, including articulated object manipulation and cloth folding. Our results demonstrate that the synthesized skill library enables the system to transfer to novel tasks with decreasing human intervention, providing a steerable and data-efficient alternative to black-box robot learning. Website: https://robo-architect.github.io/

Related papers

• Closed-Loop Verbal Reinforcement Learning for Task-Level Robotic Planning [1.6641413441634143]
  We propose a new Verbal Reinforcement Learning framework for interpretable task-level planning in mobile robotic systems.<n>The framework follows a closed-loop architecture that enables iterative policy improvement through interaction with the physical environment.<n>We validate the proposed framework on a real mobile robot performing a multi-stage manipulation and navigation task under execution uncertainty.
  arXiv  Detail & Related papers  (2026-03-23T16:28:36Z)
• Act-Observe-Rewrite: Multimodal Coding Agents as In-Context Policy Learners for Robot Manipulation [0.0]
  We present Act-Observe-Rewrite (AOR), a framework in which an LLM agent improves a robot manipulation policy.<n>AOR makes the full low-level motor control implementation the unit of LLM reasoning.<n>We report promising results, with the agent achieving high success rates without demonstrations, reward engineering, or gradient updates.
  arXiv  Detail & Related papers  (2026-03-03T22:15:55Z)
• RACER: Rich Language-Guided Failure Recovery Policies for Imitation Learning [19.023560632891467]
  We propose a scalable data generation pipeline that augments expert demonstrations with failure recovery trajectories. We then introduce Rich languAge-guided failure reCovERy (RACER), a supervisor-actor framework. Our experimental results show that RACER outperforms the state-of-the-art Robotic View Transformer on RLbench.
  arXiv  Detail & Related papers  (2024-09-23T02:50:33Z)
• Compromising Embodied Agents with Contextual Backdoor Attacks [69.71630408822767]
  Large language models (LLMs) have transformed the development of embodied intelligence. This paper uncovers a significant backdoor security threat within this process. By poisoning just a few contextual demonstrations, attackers can covertly compromise the contextual environment of a black-box LLM.
  arXiv  Detail & Related papers  (2024-08-06T01:20:12Z)
• Robotic Control via Embodied Chain-of-Thought Reasoning [86.6680905262442]
  Key limitation of learned robot control policies is their inability to generalize outside their training data.<n>Recent works on vision-language-action models (VLAs) have shown that the use of large, internet pre-trained vision-language models can substantially improve their robustness and generalization ability.<n>We introduce Embodied Chain-of-Thought Reasoning (ECoT) for VLAs, in which we train VLAs to perform multiple steps of reasoning about plans, sub-tasks, motions, and visually grounded features before predicting the robot action.
  arXiv  Detail & Related papers  (2024-07-11T17:31:01Z)
• LLaRA: Supercharging Robot Learning Data for Vision-Language Policy [56.505551117094534]
  We introduce LLaRA: Large Language and Robotics Assistant, a framework that formulates robot action policy as visuo-textual conversations.<n>First, we present an automated pipeline to generate conversation-style instruction tuning data for robots from existing behavior cloning datasets.<n>We show that a VLM finetuned with a limited amount of such datasets can produce meaningful action decisions for robotic control.
  arXiv  Detail & Related papers  (2024-06-28T17:59:12Z)
• ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning [74.58666091522198]
  We present a framework for intuitive robot programming by non-experts. We leverage natural language prompts and contextual information from the Robot Operating System (ROS) Our system integrates large language models (LLMs), enabling non-experts to articulate task requirements to the system through a chat interface.
  arXiv  Detail & Related papers  (2024-06-28T08:28:38Z)
• Interactive Planning Using Large Language Models for Partially Observable Robotics Tasks [54.60571399091711]
  Large Language Models (LLMs) have achieved impressive results in creating robotic agents for performing open vocabulary tasks. We present an interactive planning technique for partially observable tasks using LLMs.
  arXiv  Detail & Related papers  (2023-12-11T22:54:44Z)
• Language to Rewards for Robotic Skill Synthesis [37.21434094015743]
  We introduce a new paradigm that harnesses large language models (LLMs) to define reward parameters that can be optimized and accomplish variety of robotic tasks. Using reward as the intermediate interface generated by LLMs, we can effectively bridge the gap between high-level language instructions or corrections to low-level robot actions.
  arXiv  Detail & Related papers  (2023-06-14T17:27:10Z)
• Grounded Decoding: Guiding Text Generation with Grounded Models for Embodied Agents [111.15288256221764]
  Grounded-decoding project aims to solve complex, long-horizon tasks in a robotic setting by leveraging the knowledge of both models. We frame this as a problem similar to probabilistic filtering: decode a sequence that both has high probability under the language model and high probability under a set of grounded model objectives. We demonstrate how such grounded models can be obtained across three simulation and real-world domains, and that the proposed decoding strategy is able to solve complex, long-horizon tasks in a robotic setting by leveraging the knowledge of both models.
  arXiv  Detail & Related papers  (2023-03-01T22:58:50Z)
• What Matters in Language Conditioned Robotic Imitation Learning [26.92329260907805]
  We study the most critical challenges in learning language conditioned policies from offline free-form imitation datasets. We present a novel approach that significantly outperforms the state of the art on the challenging language conditioned long-horizon robot manipulation CALVIN benchmark.
  arXiv  Detail & Related papers  (2022-04-13T08:45:32Z)

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.