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arxiv: 2601.21667 · v2 · submitted 2026-01-29 · 💻 cs.RO · cs.CV

From Instruction to Event: Sound-Triggered Mobile Manipulation

Hao Ju , Shaofei Huang , Hongyu Li , Zihan Ding , Si Liu , Meng Wang , Zhedong Zheng This is my paper

Pith reviewed 2026-05-16 09:48 UTC · model grok-4.3

classification 💻 cs.RO cs.CV
keywords sound-triggered mobile manipulationauditory eventsHabitat-Echotask plannerpolicy modelsdual-source scenarioacoustic renderingmobile manipulation
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The pith

A baseline lets mobile robots detect and manipulate sound-emitting objects without explicit instructions.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper moves mobile manipulation away from reliance on predefined textual commands toward tasks where agents must perceive and act on sound-emitting objects in their environment. It introduces Habitat-Echo, a simulation platform that adds acoustic rendering to physical interactions so these sound-triggered tasks can be studied. A baseline is built from a high-level task planner and low-level policy models that together decide when and how to respond. Experiments show the baseline succeeds at autonomous detection and response, including separating a primary sound source from overlapping interference before handling the secondary object.

Core claim

By training a high-level task planner and low-level policy models inside the Habitat-Echo platform that combines acoustic rendering with physical interaction, agents can actively detect auditory events and execute manipulation sequences without receiving case-by-case textual instructions, even when two sound sources overlap.

What carries the argument

The baseline of a high-level task planner paired with low-level policy models, trained on the Habitat-Echo platform that merges acoustic rendering with physical robot interactions.

If this is right

  • Agents complete sound-triggered tasks without needing separate textual commands for each new scenario.
  • The planner and policies isolate the primary sound source from overlapping acoustic interference to execute the first interaction.
  • After the first interaction the agent continues to manipulate the secondary sound-emitting object.
  • Simulation training removes the requirement for exhaustive instruction sets while still producing robust response sequences.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If simulation-to-real transfer holds, home or factory robots could respond to unexpected sounds without per-task reprogramming.
  • Adding visual confirmation alongside auditory cues might further improve source separation in cluttered spaces.
  • The same planner-policy split could be tested on other event-driven triggers such as sudden temperature changes or motion detection.

Load-bearing premise

The acoustic rendering and physics simulation inside Habitat-Echo match real-world sound propagation and robot-object contacts closely enough that policies will indicate feasibility for physical robots.

What would settle it

Train the planner and policies in Habitat-Echo then test the same models on a physical robot in a matching room with real speakers playing the same sounds; if the robot cannot isolate and first manipulate the primary source before the secondary source, the transfer claim fails.

Figures

Figures reproduced from arXiv: 2601.21667 by Hao Ju, Hongyu Li, Meng Wang, Shaofei Huang, Si Liu, Zhedong Zheng, Zihan Ding.

Figure 1
Figure 1. Figure 1: Motivation for our work. (Upper) An example of sound-triggered mobile manipulation, the trigger signal is the sound of the event rather than instructions from humans. (Left) Instruction-based mobile manipulation needs humans to analyze the trigger signal and manually give the instruction for the robot model, which is passive. (Right) In sound-triggered mobile manipulation, the robot model automatically rec… view at source ↗
Figure 2
Figure 2. Figure 2: Illustration of the proposed tasks. (a) SonicStow requires the agent to interact with the sound source (a rigid object) via Navigate, Pick, and Place skills. (b) SonicInteract requires the agent to interact with the sound source (an articulated object) via Navigate, Open Door, and Close Sink skills. (c) Bi-Sonic Manipulation requires the agent to interact with the sound source (two objects sequentially) vi… view at source ↗
Figure 3
Figure 3. Figure 3: Overview of the proposed baseline. The sound￾triggered task planner processes initial audio-visual observations to reason and generate a high-level skill chain from the skill library. Guided by this chain, specialized policy models are sequentially activated to generate low-level actions and interact with the envi￾ronment. the target object. The agent is initialized at a random po￾sition, and target object… view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative Visualization of Task Execution. We present the execution trajectories for (a) SonicStow, (b) SonicInteract, and (c) Bi-Sonic Manipulation. For each task, the left panel illustrates the top-down view of the environment, highlighting the agent’s trajectories and the location of sound sources. The right panel displays the corresponding sequence of third-person keyframes during the execution. Note… view at source ↗
Figure 5
Figure 5. Figure 5: (c) and (d). In [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
read the original abstract

Current mobile manipulation research predominantly follows an instruction-driven paradigm, where agents rely on predefined textual commands to execute tasks. However, this setting confines agents to a passive role, limiting their autonomy and ability to react to dynamic environmental events. To address these limitations, we introduce sound-triggered mobile manipulation, where agents must actively perceive and interact with sound-emitting objects without explicit action instructions. To support these tasks, we develop Habitat-Echo, a data platform that integrates acoustic rendering with physical interaction. We further propose a baseline comprising a high-level task planner and low-level policy models to complete these tasks. Extensive experiments show that the proposed baseline empowers agents to actively detect and respond to auditory events, eliminating the need for case-by-case instructions. Notably, in the challenging dual-source scenario, the agent successfully isolates the primary source from overlapping acoustic interference to execute the first interaction, and subsequently proceeds to manipulate the secondary object, verifying the robustness of the baseline.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces sound-triggered mobile manipulation as a new task in which agents must actively detect and respond to auditory events from sound-emitting objects without explicit textual instructions. It presents Habitat-Echo, a simulation platform that combines acoustic rendering with physical interactions, and proposes a baseline consisting of a high-level task planner and low-level policy models. Experiments in simulation, including a challenging dual-source scenario, are reported to show that the baseline enables isolation of the primary source amid overlapping interference followed by manipulation of the secondary object.

Significance. If the simulation results hold under real-world conditions, the work would meaningfully advance mobile manipulation by shifting from passive instruction-driven paradigms to reactive, event-driven autonomy. The Habitat-Echo platform could become a useful benchmark for audio-visual robotics, and the baseline offers a concrete starting point for future planners and policies. The handling of dual-source interference in simulation is a notable empirical demonstration of the task's feasibility within the proposed environment.

major comments (2)
  1. [Abstract] Abstract: the claim of successful isolation of the primary source in the dual-source scenario and subsequent manipulation of the secondary object is presented without any quantitative metrics, success rates, baseline comparisons, or error analysis, leaving the empirical support for the central claim difficult to evaluate.
  2. [Experiments] Experiments section: the headline result that the baseline isolates the primary source amid overlapping acoustic interference rests entirely on Habitat-Echo; no quantitative validation of the acoustic rendering (e.g., comparison to real-room impulse responses, microphone calibration, or embodiment effects) is provided, so the reported robustness may be an artifact of the simulator rather than evidence of physical feasibility.
minor comments (1)
  1. [Introduction] The differences between the proposed task and prior audio-visual navigation or sound-source localization work could be clarified with a dedicated related-work subsection to better position the contribution.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for their insightful comments, which have helped us improve the clarity and rigor of our manuscript. We address each major comment below and indicate the revisions made.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim of successful isolation of the primary source in the dual-source scenario and subsequent manipulation of the secondary object is presented without any quantitative metrics, success rates, baseline comparisons, or error analysis, leaving the empirical support for the central claim difficult to evaluate.

    Authors: We agree with this observation. The abstract in the original submission summarized the results qualitatively. In the revised version, we will include specific quantitative metrics, such as success rates for the dual-source scenario and baseline comparisons, to provide stronger empirical support for the central claims. revision: yes

  2. Referee: [Experiments] Experiments section: the headline result that the baseline isolates the primary source amid overlapping acoustic interference rests entirely on Habitat-Echo; no quantitative validation of the acoustic rendering (e.g., comparison to real-room impulse responses, microphone calibration, or embodiment effects) is provided, so the reported robustness may be an artifact of the simulator rather than evidence of physical feasibility.

    Authors: The work presented is simulation-based, and we did not provide direct quantitative validation against real-world acoustic measurements. We will revise the experiments section to include additional details on the acoustic rendering pipeline used in Habitat-Echo and explicitly discuss the limitations regarding sim-to-real transfer. A full real-world validation is planned for future work but is outside the current scope. revision: partial

standing simulated objections not resolved
  • Quantitative validation of the acoustic rendering in Habitat-Echo against real-world data such as impulse responses or microphone calibrations.

Circularity Check

0 steps flagged

No circularity in the derivation or results chain

full rationale

The paper introduces a new task (sound-triggered mobile manipulation) and a supporting simulation platform (Habitat-Echo), then reports empirical success rates of a proposed baseline (high-level planner + low-level policies) on experiments conducted inside that platform. No equations, fitted parameters, or self-citations are described that reduce the reported outcomes (e.g., dual-source isolation) to tautologies or inputs by construction. The results are presented as new simulation experiments rather than derivations that loop back to the same fitted values or prior self-citations.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the fidelity of the new simulator and the effectiveness of the planner-policy split; no free parameters are explicitly fitted in the abstract.

axioms (1)
  • domain assumption Acoustic rendering in Habitat-Echo produces sound fields sufficiently similar to real environments for policy learning
    Invoked when claiming that simulation results indicate real-world feasibility.
invented entities (1)
  • Habitat-Echo no independent evidence
    purpose: Platform that adds acoustic rendering to physical interaction simulation for sound-triggered tasks
    Newly introduced data platform described in the abstract.

pith-pipeline@v0.9.0 · 5471 in / 1235 out tokens · 20716 ms · 2026-05-16T09:48:37.515559+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

23 extracted references · 23 canonical work pages

  1. [1]

    Additional Qualitative Result We present qualitative visualization results for the four manipulation skills in Figure 5

    Prompt for Task Planning A. Additional Qualitative Result We present qualitative visualization results for the four manipulation skills in Figure 5. Figure 5 (a) and (b) illustrate the Pick skill. In the success case (a), the agent grasps the object and correctly returns the arm to the resting position, while in the failure case (b), the arm fails to retr...

    work page

  2. [2]

    nav”, “pick

    ALLOWED SKILL VOCABULARY:You are strictly limited to ONLY the following skills. Do not invent new words or change spellings: [“nav”, “pick”, “place”, “opendoor”, “close sink”]

    work page

  3. [3]

    LOGIC MAPPING (AUDIO → SKILL):Analyze the input audio sound and classify the source object to determine the skill sequence:

    work page

  4. [4]

    nav”, “pick

    Category A: SonicStow Objects (a) If the sound source is: Mechanical Alarm, Phone (ringing tone), or Furby (toy). REQUIRED PLAN: [“nav”, “pick”, “place”]

    work page

  5. [5]

    nav”, “open door

    Category B: SonicInteract Objects (a) If the sound source is: Doorbell. REQUIRED PLAN: [“nav”, “open door”] (b) If the sound source is: Water Running. REQUIRED PLAN: [“nav”, “close sink”]

    work page

  6. [7]

    Never return an empty list

    work page

  7. [8]

    Output MUST be a valid JSON object containing a single key “plan”

    work page

  8. [10]

    plan”: [“skill 1

    OUTPUT FORMAT:{“plan”: [“skill 1”, “skill 2”, ...]} User Prompt:

    work page

  9. [11]

    Input observation provided. Based on the audio class and visual context, output the execution plan JSON

    “Input observation provided. Based on the audio class and visual context, output the execution plan JSON.” Prompt for Task Planning in Bi-Sonic Manipulation.For the Bi-Sonic Manipulation task, we organize the system prompt into five segments to handle the dual-source complexity. The initial segment lists the permissible skill vocabulary. The second detail...

    work page

  10. [12]

    nav”, “pick

    ALLOWED SKILL VOCABULARY (STRICT):You are strictly limited to ONLY the following skills: [“nav”, “pick”, “place”, “opendoor”, “close sink”]. Do not invent new words or change spellings

    work page

  11. [13]

    LOGIC MAPPING (AUDIO→SKILL):Analyze the input audio sound to determine the action sequence:

    work page

  12. [14]

    Mechanical Alarm

    Category A: SonicStow Objects (a) Sources: “Mechanical Alarm”, “Phone”, “Furby”→[“nav”, “pick”, “place”]

    work page

  13. [15]

    Doorbell

    Category B: SonicInteract Objects (a) Source: “Doorbell”→REQUIRED PLAN: [“nav”, “open door”] 13 From Instruction to Event: Sound-Triggered Mobile Manipulation (b) Source: “Running-Water”→REQUIRED PLAN: [“nav”, “close sink”]

    work page

  14. [16]

    You have to interact with it first

    OBJECT MAPPING INSTRUCTION (CRITICAL):You are provided with a partial hint about the audio content: One of the sound sources is CONFIRMED to be: ‘‘{obj 1}". You have to interact with it first. Your task is to identify the other sound source from the audio and generate the plan. You MUST map the objects exactly as follows:

    work page

  15. [17]

    first sound

    Key “first sound”: Generate the plan for‘‘{obj 1}"

    work page

  16. [18]

    second sound

    Key “second sound”: Identify the SECOND sound source from the audio (the one that is NOT ‘‘{obj 1}") and generate its plan

    work page

  17. [19]

    Do NOT output plans for any other potential objects unless they are detected as the second sound

    work page

  18. [20]

    CONSTRAINTS and OUTPUT FORMAT:

    work page

  19. [21]

    Output MUST be a valid JSON object containing a single root key “plan”

    work page

  20. [22]

    plan” MUST be exactly “first sound

    The keys inside “plan” MUST be exactly “first sound” and “second sound”

    work page

  21. [23]

    Do not include markdown formatting (like‘‘‘json), just the raw JSON string

    work page

  22. [24]

    plan”:{ “first sound

    Expected JSON Structure:{“plan”:{ “first sound”: [“skill 1”, “skill 2”, ...], #←Actions for the object assigned to ’first sound’ “second sound”: [“skill 1”, “skill 2”, ...] #←Actions for the object assigned to ’second sound’ } } User Prompt: 1.Image Input 2.Audio Input

    work page

  23. [25]

    Input observation provided. Based on the audio class and visual context, output the execution plan JSON

    “Input observation provided. Based on the audio class and visual context, output the execution plan JSON.” 14

    work page