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Silicon Language: A Robot-Native Knowledge Exchange Framework for Heterogeneous Robots

Yi Liu, Xianglin Meng, Chang Chen, Jingjing Fan

arXiv:2610.07650v1cs.RO

TL;DR

Heterogeneous robots still require substantial human adaptation to reuse one another's capabilities. Silicon Language provides a robot-centered exchange and review framework; a 30-day proof of concept offers initial evidence, while controlled comparisons remain future work.

  • Problem

    Reusing capabilities across heterogeneous robots still requires substantial human adaptation and verification because robots differ in sensors, actuators, and constraints.

    Source: §I · p. 1

  • Method

    Silicon Language has robots encode, publish, retrieve, translate, and locally review knowledge packets, with progressive blending and rollback designed to mitigate negative-transfer risk.

    Source: §I · p. 1, §III · p. 3, D. Encoding-independent usability… · p. 5

  • Results

    Two heterogeneous robots encoded and translated three capabilities across embodiments in a 30-day proof-of-concept deployment.

    Source: §VI · p. 12

  • Takeaways & Limitations

    The deployment provides initial evidence for cross-embodiment knowledge exchange, with the receiver evaluating knowledge for its own embodiment.

    Source: §VI · pp. 11–12

    The deployment involved two robots and operator-assisted file transfers, with no controlled with/without-packet or direct-reuse-versus-adapted comparisons.

    Source: G. Limitations and scope · p. 9

Abstract

from arXiv · show

Reusing a capability across heterogeneous robots still requires substantial human adaptation and verification: transferring a skill often means re-engineering interfaces, retuning parameters, and re-validating safety. We introduce Silicon Language, a robot-native knowledge exchange framework that treats the robot as the active subject of its own capability evolution. In this framework, a robot that wants a capability encodes its own experience into knowledge packets, publishes them, retrieves peer packets, translates them for its own sensors and actuators, and reviews them through independent local trial. A receiver-side usability evaluation procedure lets each robot decide for itself whether an external packet is useful, and progressive blending with automatic rollback is designed to reduce the risk of negative transfer when adopting it. The system combines three infrastructure layers (edge agent, Silicon Transfer Protocol (STP), and knowledge hub) with a capability stack inspired by the human scholarly system. We report a 30-day proof-of-concept deployment at an above-ground simulated-mine laboratory in Yulin, with following trials on an outdoor sand road and an indoor factory floor. Two heterogeneous robots encoded and translated three capabilities across embodiments through operator-assisted file copies mediated by the Silicon Language translation layer; source-side trials of the dust-locked following behavior were recorded on Taurus. Project records indicate that per-capability adaptation time dropped from days to hours; we present these figures as descriptive deployment records rather than controlled measurements. The deployment provides initial evidence for cross-embodiment knowledge exchange; fleet-level autonomous evolution and controlled with/without-packet comparisons remain future work.

I. INTRODUCTION

Silicon Language responds to the human effort required to adapt and verify skills across heterogeneous robots by making robots active participants in capability exchange. It combines a robot-led knowledge lifecycle with receiver-side evaluation, addressing gaps left by prior sharing, learning, and communication systems.

I. INTRODUCTION

  • Human-led adaptation remains substantial when robots with different sensors, actuators, and constraints reuse a capability, while human effort grows with fleets, environments, and required skills.The proposed alternative is a robot-native scholarly system in which robots encode experience, retrieve peer knowledge, translate it, review it through trial, and revise it.Source: §I · p. 1

II. RELATED WORK

  • Earlier systems address parts of robot knowledge sharing, learning, or communication, but the receiving robot does not independently adapt and judge external knowledge for its own embodiment.The framework distinguishes itself by treating knowledge as a versioned, reviewable artifact and assigning adoption decisions to the receiver.Source: §I · p. 1, §II · p. 2

C1. A robot-native, robot-centric knowledge exchange

  • Silicon Language makes robots the subjects of capability evolution, with lifecycle primitives for encoding, publishing, retrieving, translating, locally reviewing, and revising knowledge packets.The receiver decides what to adopt and how to validate it for its embodiment.Source: §I · pp. 1–2, C1. A robot-native, robot-centric… · p. 2
  • The 30-day proof of concept involved two heterogeneous robots encoding and translating capabilities, but transfers used operator-assisted file copies and adaptation-time records were descriptive, not controlled measurements.The deployment is scoped as initial evidence; fleet-scale validation and controlled comparisons remain future work.Source: C1. A robot-native, robot-centric… · p. 2

III. THE SILICON LANGUAGE FRAMEWORK

Silicon Language organizes knowledge exchange around robot agency through three infrastructure layers and a seven-layer capability stack. The robot decides what experience to encode, what packets to retrieve, and whether an incoming packet is useful and safe for its embodiment.

III. THE SILICON LANGUAGE FRAMEWORK

  • The framework combines an edge agent, Silicon Transfer Protocol, and knowledge hub with a seven-layer capability stack, a functional organization inspired by the human scholarly system.The scholarly analogy is a naming and design guide, not a claim of functional equivalence.Source: §III · p. 3
  • The capability stack comprises writing, publishing, retrieval, translation, review, versioning, and dialogue, organized from L7 through L1.The robot is intended to decide what experience to encode, which packets to retrieve, and whether incoming knowledge is useful and safe for its embodiment.Source: §III · p. 3

A. Three-layer infrastructure

Silicon Language pairs a three-layer infrastructure with a scholarly-inspired capability stack and machine-native knowledge packets. Packets separate shared content from receiver-grounded safety parameters, with the framework supporting multiple forms of knowledge.

A. Three-layer infrastructure

  • Each robot's edge agent encodes experience, evaluates performance, translates foreign packets, redeploys them with hot-swap and rollback, judges usability, and exchanges state and intent.Its six modules let the robot act as the subject of knowledge exchange.Source: A. Three-layer infrastructure · p. 3
  • STP is an application-layer packet transport protocol above TCP/IP and DDS, designed for incremental, prioritized, resumable, compressed transfers; production deployment used operator-assisted file copies instead.The knowledge hub supports edge-server, cloud, and peer-to-peer modes; the deployment used a single edge server and did not exercise gossip.Source: A. Three-layer infrastructure · pp. 3–4

B. Seven-layer capability stack

  • The seven capability-stack layers map scholarly functions to robot operations, but the mapping is a design guide rather than a claim that machine review reproduces human peer review.The capability stack is organized as writing, publishing, retrieval, translation, review, versioning, and dialogue.Source: B. Seven-layer capability stack · p. 4, Table I · p. 4

C. Knowledge packets and machine-native knowledge forms

  • A knowledge packet has a header, body, and metadata; its body may encode perceptual, spatial, behavioral, code, or meta-knowledge in an open set of machine-native forms.Safety rules travel unchanged, while safety parameters are recomputed for the receiver at least as conservatively as the publisher's and revalidated through trial.Source: C. Knowledge packets and… · p. 4

D. Encoding-independent usability evaluation

The receiver evaluates each packet through pre-screening, translation, trial, and verdict, with safety-gated adoption and rollback intended to limit negative-transfer risk. Translation depends on the packet’s body form, and the example following packet separates transferable processing logic from embodiment-specific implementation.

D. Encoding-independent usability evaluation

  • The receiver first checks applicability, environment, and hardware, then tests the adapted packet in simulation before a limited real-world trial.Trial budgets and acceptance criteria are defined per knowledge type before testing.Source: D. Encoding-independent usability… · p. 4
  • A packet failing its separate safety gate is unusable regardless of task-completion or efficiency scores; partial usability retains only verified fragments.A negative verdict is reported to the hub as a review record.Source: D. Encoding-independent usability… · p. 4
  • The dust-locked following packet transfers processing logic from Taurus to Di-Ting, while implementation code and message bindings depend on the receiving embodiment.The packet is an example of a capability produced on Taurus and adapted for Di-Ting.Source: Table II · p. 5
  • Compatible parameterized components use progressive blending, while discrete or incompatible structures use staged rollout; monitored degradation triggers restoration of the prior version.Rollback cannot undo time, energy, or physical consequences of trial execution, motivating simulation and limited-scope trials.Source: D. Encoding-independent usability… · p. 5
  • The receiver pre-screens, translates, trials, and verdicts each packet before deciding whether it is usable, partially usable, or unusable.The protocol applies across packet encodings, but translation requires body-form-specific adapters.Source: D. Encoding-independent usability… · p. 4, Figure 3 · p. 5

E. Cluster review and the fitness loop

The framework uses recipient reviews to rank and manage knowledge packets, while describing a broader fleet evolution loop that was only partly exercised in deployment. Its formal model defines fitness from reviewer-relative performance deltas, with separate safety gating and time-based decay.

E. Cluster review and the fitness loop

  • Each receiving robot independently evaluates a packet and uploads a review; the hub aggregates reports into fitness, with high-fitness packets ranked higher and low-fitness packets flagged or retired.Fitness also decays when a packet is neither retrieved nor positively reviewed.Source: E. Cluster review and the fitness loop · pp. 5–6
  • Deployment exercised versioning and single-recipient review, but multi-reviewer aggregation, branch recombination, and long-horizon fitness dynamics were not tested at fleet scale.The deployment involved two robots and one cycle; the broader loop remains a designed mechanism awaiting fleet-level validation.Source: E. Cluster review and the fitness loop · p. 6
  • The fleet loop defines variation, fitness-based selection, branch recombination, and inheritance, but deployment exercised only versioning and single-recipient review.One source-side SAFE STOP occurred during a downhill human-track failure; another safety-stop path in source code was not observed in recorded bags.Source: E. Cluster review and the fitness loop · p. 6

A. Formal model

  • Fitness aggregates reviewer-relative deltas for task completion, efficiency, and resource consumption, with positive values indicating improvement across all three measures.For resource consumption, a positive delta means consumption was reduced; normalization uses each reviewer’s own trial baseline, and cross-robot comparability remains open.Source: A. Formal model · p. 6
  • Packets without reviews remain explicitly unscored, and safety verdicts gate fitness so efficiency gains cannot offset a safety failure.The deployment’s fitness weights were set heuristically.Source: A. Formal model · p. 6
  • Fitness decay applies only to nonnegative scores; negative-fitness packets remain flagged so decay cannot spuriously improve their ranking.A new retrieval or positive report resets the decay timer.Source: A. Formal model · p. 6

B. Silicon Transfer Protocol

The Silicon Transfer Protocol (STP) is specified as an application-layer protocol above TCP/IP and DDS, with reference message types and transport topologies. The deployment did not run production STP exchanges or exercise the specified topologies.

B. Silicon Transfer Protocol

  • Reported transfers used operator-assisted file copies through the translation layer because production STP was not running.A real two-robot STP exchange was scheduled as follow-up work.Source: B. Silicon Transfer Protocol · p. 6
  • The reference design specifies proximity peer-to-peer, relay, and offline-sync topologies; offline sync is intended for mines where continuous connectivity cannot be assumed.All three topologies were specified but not exercised as runtime protocol exchanges.Source: B. Silicon Transfer Protocol · pp. 6–7
  • STP is specified above TCP/IP and DDS, and Table III maps its ten message types to capability-stack layers.The table describes a reference design, not a production deployment.Source: B. Silicon Transfer Protocol · p. 6, Table III · p. 7

A. Scenario, platforms, and human involvement

A 30-day deployment used two differently configured robots at an above-ground simulated-mine laboratory, with following trials also conducted at an outdoor sand road and indoor factory floor. Robots handled their own encoding and translation, while people set goals, supervised trials, and copied packets between robots.

A. Scenario, platforms, and human involvement

  • Four following recordings came from an outdoor sand road and one from an indoor factory floor, not the simulated-mine area.Stable following was not achieved in the simulated-mine area with the recorded dust-locked behavior.Source: A. Scenario, platforms, and human… · p. 7
  • Di-Ting is a differential-drive inspection robot, whereas Taurus is a larger Ackermann-steered transport robot with different sensors and dynamics.Their size, sensor mounting, locomotion interfaces, and braking envelopes are among the embodiment gaps the framework translates across.Source: A. Scenario, platforms, and human… · p. 7, Figure 4 · p. 7
  • Each edge agent encoded its robot’s experience and translated incoming packets, but people set goals, configured safety templates, supervised trials, and performed file transfers.The cross-robot copy was operator-assisted because production STP exchange was not running.Source: A. Scenario, platforms, and human… · p. 7
  • Operator-assisted copying is described as temporary scaffolding for the framework’s intended robot-driven exchange.The test-site protocol also kept physical e-stop authority with human supervisors.Source: A. Scenario, platforms, and human… · pp. 7–8

B. Transferred capabilities and the main case study

The paper reports three capabilities exchanged across two embodiments, with dust-locked following as the best-documented case. Taurus motion recordings provide descriptive trial evidence, while receiver-side verdicts and behavior-level metrics are unavailable.

B. Transferred capabilities and the main case study

  • The exchanged capabilities were personnel following, dust-locked lead-vehicle following, and slope and negative-obstacle awareness.The dust-locked packet encoded a nine-stage pipeline; safety rules were retained, while speed, stop-distance, and controller parameters were recomputed for the receiving chassis.Source: B. Transferred capabilities and the… · p. 8
  • The packet listed 4-D radar as an optional dust-penetrating backup, but recorded Taurus trials used lidar and RGB camera only.Radar integration was still in progress, and no radar data appear in the trial bags.Source: B. Transferred capabilities and the… · p. 8
  • Di-Ting’s translation changed the lateral controller to differential yaw-rate output and retargeted tracking to its lidar topics, but no receiver-side verdict is reported.The detailed receiver logs were unavailable, so the reported descriptive metrics are from Taurus, where the behavior originated.Source: B. Transferred capabilities and the… · p. 8

C. Recorded following trials on Taurus

  • Four Taurus motion trials covered 771.9 m in 1,024.92 s; trial 4 was operator-labelled failed after losing the lead vehicle, while the other running trials were not individually labelled.Source: C. Recorded following trials on Taurus · p. 9
  • The bags lack behavior-layer topics, so lock retention, safe-distance violations, reacquisition time, and false-positive rate were not measured.The reported velocity values are chassis feedback, and distances are integrated from velocity over bag duration.Source: C. Recorded following trials on Taurus · p. 9

D. The Di-Ting downhill failure as a negative gene

The Di-Ting downhill failure is preserved as a negative gene with a slope-based pre-screening rule. The deployment also documents safety-stop paths and descriptive development-time records, without validating adoption rollback or establishing controlled speedup.

D. The Di-Ting downhill failure as a negative gene

  • At slopes above the observed trigger threshold of approximately 8°, receiving robots should not copy the original ground-removal height threshold blindly.The downhill pitch tilted the groundplane estimate, clipped the person’s lower body, and caused a partial OBB to fail the human-size gate before SAFE STOP.Source: D. The Di-Ting downhill failure as a… · p. 9

E. SAFE STOP and rollback paths

  • RB-001 was an observed Di-Ting source-side SAFE STOP, while RB-002 was coded but unobserved; neither validates cross-robot adoption rollback.The framework’s adoption-rollback mechanism remained unexercised in the recorded deployment.Source: E. SAFE STOP and rollback paths · p. 9

F. Development time and code scale

  • Development-time records report raw calendar times, but the modes are not like-for-like and do not establish a framework-specific speedup.Manual and human+LLM modes include from-scratch development, whereas Silicon Language records adaptation of an existing capability.Source: F. Development time and code scale · p. 9
  • The audited deployed codebase contains 75,560 shipped lines across 379 files, excluding third-party drivers.The count includes deployed Simulink-generated C and does not support the previously cited 300,000-line figure.Source: F. Development time and code scale · p. 9

G. Limitations and scope

The deployment is a limited proof of concept: it involved two robots, one 30-day period, and operator-assisted transfers, without controlled comparisons or behavior-layer validation. Source code, raw ROS bags, and detailed logs are not publicly released; limited anonymized access may be available by agreement.

G. Limitations and scope

  • The proof of concept covers two robots, one simulated-mine environment, and 30 days; transfers were operator-assisted, not runtime STP exchanges.No controlled with/without-packet or direct-reuse-versus-adapted comparisons attribute observed motion to the framework.Source: G. Limitations and scope · p. 9
  • Recorded motion quantities do not validate following behavior because behavior-layer measures such as lock retention and safe-distance compliance are absent.The velocity figure illustrates commanded and measured chassis velocity, not lock retention or tracking error, because the bags lack the behavior-layer target state.Source: G. Limitations and scope · p. 9, Figure 6 · p. 10

H. Code and data availability

  • Source code, raw ROS bags, and detailed deployment logs are not publicly released because they contain proprietary, site-specific, and operator-action information.Qualified researchers may contact the corresponding author for limited, agreement-bound access to anonymized excerpts.Source: H. Code and data availability · pp. 9–10

VI. CONCLUSION

Silicon Language makes the robot the subject of capability exchange: it translates and reviews peer knowledge through local trial, with progressive blending and rollback designed to mitigate negative-transfer risk. A 30-day proof-of-concept deployment demonstrated cross-embodiment encoding and translation, but not fleet-level autonomous evolution or framework-attributed performance.

VI. CONCLUSION

  • The five Taurus trial motion metrics are descriptive and do not by themselves attribute following performance to Silicon Language.The accompanying frames provide qualitative evidence of a loss event, not quantitative perception-accuracy claims.Source: Table V · p. 11, Figure 8 · p. 11
  • Silicon Language combines an edge agent, STP, and knowledge hub with receiver-side usability evaluation; progressive blending and rollback are designed to mitigate negative-transfer risk.The receiver searches, translates packets to its sensors and actuators, and judges their usefulness and safety through its own trial.Source: §VI · pp. 11–12
  • Two heterogeneous robots encoded and translated three capabilities across embodiments during a 30-day proof of concept, using operator-assisted file copies.The Taurus following trials were recorded on an outdoor sand road and indoor factory floor, not in the simulated mine; adaptation-time records were descriptive and non-controlled.Source: §VI · pp. 12–13
  • The deployment does not establish fleet-level autonomous evolution or attribute observed motion to the framework through controlled with/without-packet comparisons.Multi-reviewer aggregation, branch recombination, and long-horizon fitness dynamics also remain unvalidated at scale.Source: §VI · p. 13

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