PaperlayerView exact revision on arXiv

Source-linked AI summary

Chat with the Environment: Interactive Multimodal Perception Using Large Language Models

Xufeng Zhao, Mengdi Li, Cornelius Weber, Muhammad Burhan Hafez, Stefan Wermter

arXiv:2303.08268v2cs.ROcs.AIcs.CLcs.LGcs.SDeess.AS

TL;DR

The system should avoid getting bogged down in details and respond to resulting sensations in the context of a specific task. Matcha combines an LLM with modular multimodal perception and a language-conditioned policy, using language feedback to support interactive planning and execution. Matcha achieves a 90.57% success rate compared with the 89.18% ideal theory baseline, and 56.67% success with indistinct impact-sound descriptions versus 33.33% chance.

  • Problem

    The system should avoid getting bogged down in details and respond to resulting sensations in the context of a specific task.

    Source: §I · p. 1

  • Method

    Matcha combines an LLM with modular multimodal perception and a language-conditioned policy, using language feedback to support interactive planning and execution.

    Source: A. Architecture · p. 3, 1) Vision · p. 3

  • Results

    Matcha achieves a 90.57% success rate compared with the 89.18% ideal theory baseline, and 56.67% success with indistinct impact-sound descriptions versus 33.33% chance.

    Source: B. Results · p. 5

  • Takeaways & Limitations

    The authors conclude that LLM commonsense and modularity and flexibility support reasonable interactive multimodal perception and generalization in the evaluated task.

    Source: §VI · p. 7

    Interpreting the real world with language can be limited by task and environmental complexity.

    Source: §V · p. 6

Abstract

from arXiv · show

Programming robot behavior in a complex world faces challenges on multiple levels, from dextrous low-level skills to high-level planning and reasoning. Recent pre-trained Large Language Models (LLMs) have shown remarkable reasoning ability in few-shot robotic planning. However, it remains challenging to ground LLMs in multimodal sensory input and continuous action output, while enabling a robot to interact with its environment and acquire novel information as its policies unfold. We develop a robot interaction scenario with a partially observable state, which necessitates a robot to decide on a range of epistemic actions in order to sample sensory information among multiple modalities, before being able to execute the task correctly. An interactive perception framework is therefore proposed with an LLM as its backbone, whose ability is exploited to instruct epistemic actions and to reason over the resulting multimodal sensations (vision, sound, haptics, proprioception), as well as to plan an entire task execution based on the interactively acquired information. Our study demonstrates that LLMs can provide high-level planning and reasoning skills and control interactive robot behavior in a multimodal environment, while multimodal modules with the context of the environmental state help ground the LLMs and extend their processing ability. The project website can be found at \href{https://matcha-model.github.io}{\textcolor{blue}{https://matcha-model.github.io/}}.

I. INTRODUCTION

Robots must select relevant sensory information and respond to it in the context of a task, but passive perception cannot resolve every uncertainty. Interactive perception lets a robot actively gather information, though it adds system complexity.

I. INTRODUCTION

  • Robots should attend selectively to sensory stimuli and respond to resulting sensations in the context of a specific task.Source: §I · p. 1

A. Interactive Multimodal Perceptions

B. Chatting with the Environment

Matcha uses an LLM to plan and respond to environmental feedback, while modular perceptions translate sensory data into language the LLM can process. The framework is intended to support interactive perception and behavior explanation.

B. Chatting with the Environment

  • Because LLMs cannot directly process application-specific raw multimodal data, separately trained perception modules translate sensations into natural-language descriptions.Source: B. Chatting with the Environment · p. 2
  • The paper establishes a manipulation scenario with multimodal sensory data and language descriptions, and presents Matcha for interactive multimodal perception and behavior explanation.Source: B. Chatting with the Environment · p. 2
  • Matcha is a multimodal environment chatting agent that gives an LLM continuous access to environmental feedback for contextual reasoning and planning.Source: B. Chatting with the Environment · p. 2

II. RELATED WORK

Prior work studies multimodal learning and robotic information gathering, as well as LLM-based robotic planning. Matcha’s distinction is a feedback loop in which LLM planning uses actively collected environmental information.

II. RELATED WORK

  • Robotics research uses multimodality because one sensory channel can carry information that another cannot reveal.Source: §II · p. 2
  • Robotic information gathering selects perceptual actions to reduce uncertainty, while numerical multimodal fusion can be difficult to transfer across applications.Source: §II · p. 2
  • Earlier LLM robotic planners select commands from instructions and built-in knowledge, but do not adapt decisions using actively collected environmental information.Source: §II · p. 2

A. Architecture

Matcha combines an LLM, multimodal perception modules, and a low-level execution policy, linked by language. The LLM selects information-gathering commands, receives semantic descriptions of the environment, and plans from that feedback.

A. Architecture

  • Matcha interactively uses multimodal perception when passive vision provides insufficient information, repeating epistemic actions until information is sufficient.Source: A. Architecture · pp. 2–3
  • The framework links an LLM backbone, multimodal perception modules, and a low-level command execution policy through natural language.Source: A. Architecture · p. 3, Figure 2 · p. 3
  • After a perceiving command executes, the corresponding module converts the environmental response into semantic descriptions that return to the LLM for planning.Source: A. Architecture · p. 3
  • The LLM uses few-shot prompting without fine-tuning, while policy and perception modules can be designed separately and plugged in as needed.Source: A. Architecture · p. 3

B. Multimodal Perception and Execution Policy

1) Vision:

The perception modules turn visual, sound, and weight feedback into language the LLM can use to understand the scene and reason about objects.

1) Vision:

  • ViLD detects object categories and positions, which are used to identify objects for policy execution.The scene is presented to the LLM in a prompt describing the detected objects.Source: 1) Vision · p. 3

2) Impact Sound:

  • Impact-sound feedback is classified and expressed in natural language, with either material-specific or less-distinct descriptions.A knock-on action can produce the sound input; the less-distinct option uses low-level descriptions such as “tinkling.”Source: 2) Impact Sound · p. 3

3) Weight:

  • After a weighing action, torque measurements are translated into natural-language descriptions of an object's weight.Descriptions can be qualitative, such as “It is lightweight,” or numerical, such as “It weighs 30g”.Source: 3) Weight · p. 3

4) Haptics:

The framework represents haptic feedback in language, executes LLM-generated commands through a policy, and uses constrained prompts to ground planning in the robot’s capabilities.

4) Haptics:

  • Haptic feedback is represented with abstract force-torque descriptions, such as whether an object feels soft or hard and smooth.The approach uses abstract descriptions rather than a costly high-resolution tactile sensor.Source: 4) Haptics · p. 3

5) Execution Policy:

  • The execution policy locates the LLM-identified target, carries out its actionable command, and collects environmental feedback for perception modules.Execution is conditioned on both the generated command and visual information.Source: 5) Execution Policy · p. 4

C. Prompt Engineering

  • Few-shot prompts define executable commands and task examples, but only sufficiently large language models reliably follow their strict formatting constraints.Smaller models may generate unexpected commands that require extra tuning; the constrained prompting approach is described as flexible to new actions or perception modules.Source: C. Prompt Engineering · p. 4

IV. EXPERIMENTS

The experiments test whether Matcha can use multimodal feedback to identify an object by its visually indistinguishable material and then pick it up.

A. Experimental Setup

  • The task requires picking an object by its material, which is not visually distinguishable under the experimental settings.The setup is designed so a single modality may be insufficient, while other modalities can compensate for ambiguity.Source: A. Experimental Setup · p. 4
  • In CoppeliaSim, the NICOL robot operates among randomly selected blocks and can knock, touch, weigh, and pick them up.The first three actions gather impact-sound, haptic, and weight information; picking up the object completes the task.Source: A. Experimental Setup · p. 4
  • Because CoppeliaSim lacks physics-driven sound and deformable-object simulation, the setup uses alternatives for haptics and impact sounds.Haptic descriptions are assigned according to material, while an auditory classifier is trained on YCB impact sounds and test clips are randomly loaded in place of runtime simulation.Source: A. Experimental Setup · pp. 4–5
  • The experiments compare distinct, material-specific sound descriptions with indistinct descriptions, and evaluate Matcha across 50 randomly generated scenarios per setting.The distinct descriptions provide material labels and classifier confidence; indistinct descriptions use common, potentially ambiguous sound terms.Source: A. Experimental Setup · p. 5

B. Results

Matcha uses information across modalities to identify target materials, including when sound descriptions are ambiguous, while performance depends on the LLM’s ability to follow executable-command constraints.

B. Results

  • 90.57%: Matcha’s success rate exceeds the 89.18% ideal sound-only baseline by using compensatory information from other modalities.The 89.18% baseline is derived from a reasonable sound-only rule and is treated as an upper bound for sound-only Matcha.Source: B. Results · p. 5
  • 56.67%: Matcha succeeds with indistinct sound descriptions, above the 33.33% chance rate for randomly choosing among three objects.Failure cases include glass and ceramic, whose sound, haptic, and weight descriptions are similar; distinct descriptions preserve material predictions from the sound classifier.Source: B. Results · p. 5
  • Only the strong text-davinci-003 model reliably follows the primary prompt, while weaker text-ada-001 may generate commands the robot cannot execute.Examples include commands with an unsupported number of targets or parameters.Source: B. Results · p. 5

C. Case Studies

The case studies show Matcha using multimodal interaction to infer latent material properties, while also exposing a failure when glass and ceramic remain difficult to distinguish.

C. Case Studies

  • Matcha deduces the latent material from indistinct multimodal descriptions while stopping exploration of the red block and examining the green block exhaustively.Fig. 3 presents a successful fiber-material deduction.Source: Figure 3 · p. 6, C. Case Studies · p. 6
  • Weight and haptic interaction lets Matcha distinguish the orange block from plastic after its impact sound suggests plastic rather than metal.The paper attributes the correction to interactive multimodal perception.Source: C. Case Studies · p. 6
  • With indistinct sound descriptions, Matcha mistakes a ceramic block for the requested glass block because their sensing descriptions are similar.The case study links the error to difficulty resolving epistemic uncertainty.Source: C. Case Studies · p. 6

D. Discussion

The discussion highlights how multimodal evidence and LLM knowledge can support material reasoning, while noting failures under ambiguous sensing and long reasoning chains.

D. Discussion

  • LLM common sense supports instruction flexibility, including linking an object’s multimodal perceptions to a requested utility such as cracking a nut.The example varies the instruction from “the metal block” to “a block that may be suitable for cracking a nut”.Source: D. Discussion · p. 6
  • Multimodal perception corrects a sound-based misclassification in the Fig. 4 example.The sound description alone can suggest the wrong material.Source: Figure 4 · p. 6
  • Indistinct sound descriptions can leave glass and ceramic unresolved, producing a failure when the agent selects ceramic instead of glass.The discussion attributes this to sensing similarity.Source: Figure 5 · p. 6
  • LLMs may prematurely conclude from long lists of facts, and their reasoning traces may not align with human expectations.Source: D. Discussion · p. 6

V. GENERALIZATION, LIMITATION AND FUTURE WORK

The paper presents Matcha’s modularity and LLM commonsense as supporting generalization, while identifying language-based reasoning, scene description, and local model costs as limitations or future-work concerns.

V. GENERALIZATION, LIMITATION AND FUTURE WORK

  • Matcha’s LLM commonsense is presented as supporting generalizability, whereas reinforcement-learning control may require massive datasets or interactions to learn common sense.The paper also characterizes such learning as less efficient and generalizable.Source: §V · p. 6
  • Complex tasks and environment dynamics may require decomposition and repeated flexible vision-to-language queries, while multimodal LLM descriptions are less controllable and accurate than templated modules.The paper identifies this mismatch with static language expression as a challenge.Source: §V · pp. 6–7
  • Future work will explore multimodal models to leverage unified features.The paper frames this as a direction despite current limitations.Source: §V · p. 7

VI. CONCLUSIONS

  • The conclusion reports that Matcha performs interactive multimodal perception reasonably by using LLM commonsense, with generalizability attributed to modularity and flexibility.Source: §VI · p. 7
  • Training and maintaining strong LLMs locally is currently costly because of their computation and memory requirements.The paper proposes distilling domain-specific knowledge into more manageable local models and further investigating prompt engineering and multimodal LLMs.Source: §VI · p. 7
Loading 2303.08268v2…