self-improving-robotics

name

self-improving-robotics

description

Captures robotics autonomy failures, operational incidents, and engineering learnings to enable continuous improvement across perception, localization, planning, control, simulation, safety, and hardware integration. Use when: (1) Robot fails to localize in dynamic environment, (2) Planner fails in narrow passage or obstacle-rich scene, (3) Oscillatory control behavior or unstable PID tuning appears, (4) Sensor desync occurs (camera-lidar-imu timestamp mismatch), (5) Hardware driver drops packets or CAN timeout occurs, (6) Safety stop or emergency brake triggers unexpectedly, (7) Simulation succeeds but real robot fails, (8) Thermal throttling, battery sag, or power brownout appears.

Self-Improving Robotics Skill

Log robotics-specific learnings, incidents, and feature requests to markdown files for continuous improvement. This skill captures localization drift, planning failures, control instability, sensor fusion issues, hardware interface faults, safety boundary violations, sim-to-real gaps, and power/thermal constraints.

Important learnings get promoted to durable operational artifacts:

• Safety checklists
• Calibration playbooks
• Tuning runbooks
• Robotics sections in SOUL.md, AGENTS.md, and TOOLS.md

First-Use Initialisation

Before logging anything, ensure the .learnings/ directory and files exist in the project or workspace root. If any are missing, create them:

mkdir -p .learnings
[ -f .learnings/LEARNINGS.md ] || printf "# Robotics Learnings\n\nLocalization drift events, planning failures, control instabilities, sensor fusion errors, hardware interface issues, safety boundary violations, sim-to-real gaps, and power/thermal constraints captured during robotics engineering work.\n\n**Categories**: localization_drift | planning_failure | control_instability | sensor_fusion_error | hardware_interface_issue | safety_boundary_violation | sim_to_real_gap | power_thermal_constraint\n**Areas**: perception | localization | mapping | planning | control | manipulation | navigation | safety | simulation | hardware_integration\n\n---\n" > .learnings/LEARNINGS.md
[ -f .learnings/ROBOTICS_ISSUES.md ] || printf "# Robotics Issues Log\n\nRecurring robotics failures, autonomy incidents, and subsystem-level defects.\n\n---\n" > .learnings/ROBOTICS_ISSUES.md
[ -f .learnings/FEATURE_REQUESTS.md ] || printf "# Feature Requests\n\nRobotics tooling, automation, and autonomy capability requests.\n\n---\n" > .learnings/FEATURE_REQUESTS.md

Never overwrite existing files. This is a no-op if .learnings/ is already initialised.

Do not log secrets, private keys, or sensitive infrastructure endpoints. Prefer redacted excerpts for logs, telemetry, and stack traces.

If you want automatic reminders, use the opt-in hook workflow described in Hook Integration.

Quick Reference

Situation	Action
Robot fails to localize in dynamic environment	Log issue to .learnings/ROBOTICS_ISSUES.md and learning to .learnings/LEARNINGS.md with localization_drift
Planner fails in narrow passage / obstacle-rich scene	Log to .learnings/ROBOTICS_ISSUES.md with reproducible scene metadata
Oscillatory control behavior / unstable PID tuning	Log to .learnings/ROBOTICS_ISSUES.md with control loop telemetry and frequency
Sensor desync (camera-lidar-imu timestamp mismatch)	Log to .learnings/ROBOTICS_ISSUES.md and tag sensor_fusion_error
Driver drops packets / CAN timeout / serial disconnect	Log to .learnings/ROBOTICS_ISSUES.md with bus and firmware details
Safety stop or emergency brake unexpectedly triggered	Log to .learnings/ROBOTICS_ISSUES.md with safety state transitions
Simulation success but real robot failure	Log to .learnings/LEARNINGS.md with category sim_to_real_gap
Thermal throttling / battery sag / brownout	Log to .learnings/LEARNINGS.md with category power_thermal_constraint
New capability needed for robotics workflow	Log to .learnings/FEATURE_REQUESTS.md
Recurring issue (3+ occurrences)	Link entries, increase priority, and promote to runbook/checklist
Broadly applicable autonomy pattern	Promote to safety checklist, calibration playbook, tuning runbook, or core prompt docs

OpenClaw Setup (Recommended)

OpenClaw is the primary platform for this skill. It uses workspace-based prompt injection with automatic skill loading.

Installation

Via ClawdHub (recommended):

clawdhub install self-improving-robotics

Manual clone:

git clone https://github.com/jose-compu/self-improving-robotics.git ~/.openclaw/skills/self-improving-robotics

Workspace Structure

OpenClaw injects these files into every session:

~/.openclaw/workspace/
├── AGENTS.md          # Multi-agent workflows and delegation
├── SOUL.md            # Behavioral and safety guardrails
├── TOOLS.md           # Tooling usage and integration notes
├── MEMORY.md          # Long-term memory (main session only)
├── memory/            # Daily memory files
│   └── YYYY-MM-DD.md
└── .learnings/        # This skill's log files
    ├── LEARNINGS.md
    ├── ROBOTICS_ISSUES.md
    └── FEATURE_REQUESTS.md

Create Learning Files

mkdir -p ~/.openclaw/workspace/.learnings

Then create the log files (or copy from assets/):

• LEARNINGS.md - category-based learnings
• ROBOTICS_ISSUES.md - concrete incidents and failures
• FEATURE_REQUESTS.md - tooling and capability requests

Promotion Targets

When robotics learnings prove broadly applicable, promote them:

Learning Type	Promote To	Example
Safety-critical incident patterns	Safety checklist	"Require sensor freshness checks before enabling autonomous motion"
Calibration/sync workflows	Calibration playbook	"Camera-lidar-imu time sync verification sequence"
Stable tuning methods	Tuning runbook	"PID retune order under payload changes"
Robotics behavior principles	SOUL.md	"Never bypass safety interlocks for speed"
Multi-agent robotics workflow	AGENTS.md	"Separate sim, HIL, and field validation owners"
Tooling integration standards	TOOLS.md	"Always record synchronized telemetry bundles"
Reusable failure mitigations	New reusable skill	"narrow-passage-planning-resilience"

Optional: Enable Hook

For automatic reminders at session start:

cp -r hooks/openclaw ~/.openclaw/hooks/self-improving-robotics
openclaw hooks enable self-improving-robotics

See references/openclaw-integration.md for complete details.

---

Generic Setup (Other Agents)

For Claude Code, Codex, Copilot, or other agents, create .learnings/ in project or workspace root:

mkdir -p .learnings

Create files using the headers shown in this skill.

Logging Format

Use entry types:

• LRN = learning
• ROB = robotics issue
• FEAT = feature request

Learning Entry [LRN-YYYYMMDD-XXX]

Append to .learnings/LEARNINGS.md:

## [LRN-YYYYMMDD-XXX] category

**Logged**: ISO-8601 timestamp
**Priority**: low | medium | high | critical
**Status**: pending
**Area**: perception | localization | mapping | planning | control | manipulation | navigation | safety | simulation | hardware_integration

### Summary
One-line description of the learning

### Details
Full context: what was observed, why it happened, what constraints mattered,
and how the mitigation should be applied. Include specific telemetry indicators.

### Suggested Action
Concrete process or config update to apply

### Metadata
- Source: telemetry_alert | simulation_failure | hil_test | field_test | safety_event | hardware_diagnostics | code_review | postmortem
- Platform: robot_model_or_stack (optional)
- Related Files: path/to/config_or_code
- Tags: tag1, tag2
- See Also: LRN-20250110-001 (if related)
- Pattern-Key: localization_drift.dynamic_scene (optional)
- Recurrence-Count: 1 (optional)
- First-Seen: 2025-01-15 (optional)
- Last-Seen: 2025-01-15 (optional)

---

Categories for learnings:

Category	Use When
localization_drift	Pose confidence degrades over time or in dynamic scenes
planning_failure	Planner repeatedly cannot find safe/feasible path
control_instability	Oscillation, overshoot, divergence, or instability appears
sensor_fusion_error	Multi-sensor alignment/timing inconsistency corrupts estimate
hardware_interface_issue	Driver, bus, transport, or actuator interface defects
safety_boundary_violation	Safety envelope trigger or near-violation occurs
sim_to_real_gap	Simulation passes but field behavior fails
power_thermal_constraint	Thermal, voltage, or energy limits degrade behavior

Robotics Issue Entry [ROB-YYYYMMDD-XXX]

Append to .learnings/ROBOTICS_ISSUES.md:

## [ROB-YYYYMMDD-XXX] issue_name

**Logged**: ISO-8601 timestamp
**Priority**: high
**Status**: pending
**Area**: perception | localization | mapping | planning | control | manipulation | navigation | safety | simulation | hardware_integration

### Summary
Brief description of the robotics issue

### Error Output

Command output, safety alerts, diagnostics, or stack traces (redacted/summarized)

### Root Cause
What subsystem or integration issue caused the failure.

### Fix
Specific mitigations, parameter adjustments, code updates, or operational changes.

### Prevention
How to avoid recurrence (checklist guardrail, test, monitor, fallback behavior).

### Context
- Trigger: telemetry_alert | simulation_failure | hil_test | field_test | safety_event | hardware_diagnostics
- Environment: sim | lab | warehouse | outdoors | unknown
- Run ID or mission segment

### Metadata
- Reproducible: yes | no | unknown
- Related Files: path/to/config_or_code
- See Also: ROB-20250110-001 (if recurring)

---

Feature Request Entry [FEAT-YYYYMMDD-XXX]

Append to .learnings/FEATURE_REQUESTS.md:

## [FEAT-YYYYMMDD-XXX] capability_name

**Logged**: ISO-8601 timestamp
**Priority**: medium
**Status**: pending
**Area**: perception | localization | mapping | planning | control | manipulation | navigation | safety | simulation | hardware_integration

### Requested Capability
What robotics capability, tool, or automation is needed

### User Context
Why it is needed and what workflow it improves

### Complexity Estimate
simple | medium | complex

### Suggested Implementation
How this could be built (tooling, pipeline, monitor, playbook, simulation harness)

### Metadata
- Frequency: first_time | recurring
- Related Features: existing_tool_or_feature

---

ID Generation

Format: TYPE-YYYYMMDD-XXX

• TYPE: LRN (learning), ROB (robotics issue), FEAT (feature request)
• YYYYMMDD: current date
• XXX: sequential number or random 3 chars (example: 001, A7B)

Examples: LRN-20250415-001, ROB-20250415-A3F, FEAT-20250415-002

Resolving Entries

When an issue is fixed, update the entry:

1. Change **Status**: pending to **Status**: resolved
2. Add a resolution block after Metadata:

### Resolution
- **Resolved**: 2025-01-16T09:00:00Z
- **Commit/PR**: abc123 or #42
- **Validation**: simulation + HIL + controlled field test
- **Notes**: Parameter retuned, watchdog threshold adjusted, and safety test added

Other status values:

• in_progress - actively being diagnosed or fixed
• wont_fix - intentionally not addressed (add reason in Resolution notes)
• promoted - elevated to checklist, playbook, runbook, or core docs
• promoted_to_skill - extracted as reusable skill

Detection Triggers

Automatically log when you encounter:

Localization degradation (issue + learning):

• Robot fails to localize in dynamic environment
• Covariance growth or pose jump after sensor occlusion
• Repeated relocalization events in stable map zones
• Odom drift divergence against fused pose estimate

Planning failures (issue + learning):

• Planner fails in narrow passage
• No valid path in obstacle-rich scenes
• Recovery loops exceed threshold
• Frequent oscillation between alternative paths

Control instability (issue + learning):

• Oscillatory control behavior appears
• Unstable PID tuning under payload changes
• Command saturation and watchdog warnings
• Tracking error diverges during low-speed maneuvers

Sensor fusion anomalies (issue + learning):

• Camera-lidar-imu timestamp mismatch
• Out-of-order sensor packets
• Frame transform inconsistencies
• Fusion jumps due to delayed sensor stream

Hardware interface faults (issue + learning):

• Driver drops packets
• CAN timeout bursts
• Serial disconnects
• Motor driver overcurrent faults

Safety incidents (issue + learning):

• Safety stop unexpectedly triggered
• Emergency brake event without true obstacle
• Boundary monitor false positives
• Interlock state transitions out of sequence

Sim-to-real mismatches (learning):

• Simulation success but real robot failure
• Sensor noise or latency assumptions differ from field
• Domain randomization lacks critical disturbances
• Control policy brittle under real-world traction or dynamics

Power and thermal limits (learning):

• Thermal throttling under sustained workload
• Battery sag causes degraded control rate
• Brownout events during high actuator demand
• Compute frequency throttling impacts planner timing

Priority Guidelines

Priority	When to Use	Robotics Examples
critical	Safety risk, collision potential, emergency stop instability, hardware damage risk	Emergency stop false negatives, repeated collision near-miss, motor overcurrent damage risk
high	Recurring failure that blocks autonomy missions	Planner fails in narrow corridor, localization drift in common dynamic scenes
medium	Significant but recoverable issue with available fallback	Sensor desync occasional spikes, moderate control oscillation with fallback enabled
low	Minor tuning or observability improvement	Dashboard labeling mismatch, non-critical telemetry naming cleanup

Area Tags

Use area tags to filter and route issues:

Area	Scope
perception	Object detection, feature extraction, sensor preprocessing
localization	Pose estimation, drift correction, relocalization strategies
mapping	Map quality, occupancy updates, semantic map consistency
planning	Global/local planning, feasibility, obstacle avoidance
control	PID/MPC/controller behavior, stability, actuator command tracking
manipulation	Grasp planning, arm trajectories, end-effector behavior
navigation	End-to-end mission routing and navigation state transitions
safety	Safety monitors, interlocks, emergency stop logic, boundaries
simulation	Simulator fidelity, scenario generation, domain randomization
hardware_integration	Drivers, buses, firmware, power systems, interfaces

Promoting to Permanent Robotics Standards

When a learning is broadly applicable (not a one-off fix), promote it to permanent project standards.

When to Promote

• Same failure pattern appears in 2+ missions or sites
• Mitigation proves stable across sim and real robot
• Safety impact warrants mandatory guardrail
• Reproducible diagnostics workflow saves repeated debugging time

Promotion Targets

Target	What Belongs There
Safety checklist	Mandatory pre-run and post-incident safety checks
Calibration playbook	Repeatable calibration/time-sync procedures
Tuning runbook	Stable controller/planner tuning workflow and limits
SOUL.md	Robotics behavior principles and non-negotiable guardrails
AGENTS.md	Multi-agent validation workflows and decision steps
TOOLS.md	Telemetry, replay, simulation, and diagnostics tooling guidance
Simulation test suite docs	Scenario matrices and acceptance thresholds

How to Promote

1. Distill the learning into concise operational rule or checklist item
2. Place it in the right target document
3. Update original entry:
   • Change **Status**: pending to **Status**: promoted
   • Add **Promoted**: safety checklist (or calibration playbook, tuning runbook, SOUL.md, AGENTS.md, TOOLS.md)

Recurring Pattern Detection

If logging something similar to an existing entry:

1. Search first: rg "keyword" .learnings/
2. Link entries: add **See Also**: ROB-20250110-001 in Metadata
3. Bump priority if recurrence grows and fallback weakens
4. Escalate systemic fix when recurrence suggests process or tooling gaps.

Pattern Keys and Recurrence

Use Pattern-Key for dedupe, such as:

• localization_drift.dynamic_aisle
• planning_failure.narrow_passage_clearance
• control_instability.low_speed_heading
• sensor_fusion_error.timestamp_skew

When recurrence is tracked:

• Increment Recurrence-Count
• Update Last-Seen
• Add See Also links between related LRN and ROB entries

Periodic Review Cadence

Review .learnings/ at natural robotics development milestones:

When to Review

• Before new field deployment
• After each incident postmortem
• At end of each tuning cycle
• Weekly during active autonomy development
• Before releasing updated safety or calibration docs

Quick Status Check

# Count pending robotics entries
rg "Status\\*\\*: pending" .learnings/*.md | wc -l

# List high-priority unresolved robotics issues
rg -n "Priority\\*\\*: high|Priority\\*\\*: critical" .learnings/ROBOTICS_ISSUES.md

# Find all localization learnings
rg -n "localization_drift|Area\\*\\*: localization" .learnings/*.md

# Find unresolved safety incidents
rg -n "Area\\*\\*: safety" .learnings/ROBOTICS_ISSUES.md

Review Actions

• Resolve fixed incidents and add validation evidence
• Promote recurring patterns to checklist/playbook/runbook
• Link related entries across learnings and issues
• Archive stale low-priority items if explicitly wont_fix
• Convert strong patterns into reusable skills

Best Practices

1. Safety first - if there is risk to people, environment, or hardware, log and escalate immediately
2. Capture synchronized telemetry - include timestamps and subsystem context, not isolated log snippets
3. Record reproducibility conditions - include robot model, firmware, map, payload, and environment
4. Use staged validation - simulation -> HIL -> controlled field before declaring resolved
5. Separate symptom from root cause - emergency stop is symptom; stale safety mask may be cause
6. Preserve fallback behavior context - note which fallback mitigated risk and where it failed
7. Track sim-to-real assumptions - list what simulator omitted or simplified
8. Treat power and thermal as first-class constraints - degraded compute can mimic software defects
9. Prefer minimal, actionable entries - concise and precise entries are easier to promote
10. Promote aggressively when recurrence appears - repeated failures should become standards, not isolated notes

Resolution Workflow

Follow this workflow for each ROB or LRN item:

1. Triage
   • classify severity and safety impact
   • assign area and category
2. Reproduce
   • replicate in closest safe environment
   • capture deterministic evidence when possible
3. Diagnose
   • isolate subsystem boundaries
   • verify whether failure is data, model, control, hardware, or integration
4. Mitigate
   • apply smallest safe change first
   • add guardrail if risk remains
5. Validate
   • run simulation/HIL/field checks per severity
   • verify no regression in adjacent subsystem
6. Document
   • update status and resolution block
   • link related entries and promotion target if warranted

Hook Integration

Enable automatic reminders through agent hooks. This is opt-in.

Quick Setup (Claude Code / Codex)

Create .claude/settings.json in your project:

{
  "hooks": {
    "UserPromptSubmit": [{
      "matcher": "",
      "hooks": [{
        "type": "command",
        "command": "./skills/self-improving-robotics/scripts/activator.sh"
      }]
    }]
  }
}

This injects a robotics-focused reminder after each prompt (~50-100 tokens overhead).

Advanced Setup (With Error Detection)

{
  "hooks": {
    "UserPromptSubmit": [{
      "matcher": "",
      "hooks": [{
        "type": "command",
        "command": "./skills/self-improving-robotics/scripts/activator.sh"
      }]
    }],
    "PostToolUse": [{
      "matcher": "Bash",
      "hooks": [{
        "type": "command",
        "command": "./skills/self-improving-robotics/scripts/error-detector.sh"
      }]
    }]
  }
}

Enable PostToolUse when you want automatic scan of command output for robotics/autonomy failure patterns.

Available Hook Scripts

Script	Hook Type	Purpose
scripts/activator.sh	UserPromptSubmit	Reminds to evaluate robotics learnings
scripts/error-detector.sh	PostToolUse (Bash)	Detects common robotics/autonomy error terms in output

See references/hooks-setup.md for complete setup and troubleshooting.

Automatic Skill Extraction

When a robotics learning is valuable enough to become reusable, extract it.

Skill Extraction Criteria

Criterion	Description
Recurring	Same failure mode appears in 2+ subsystems, robots, or deployments
Verified	Status is resolved with validated mitigation
Non-obvious	Required meaningful diagnosis beyond trivial config typo
Broadly applicable	Useful across projects or autonomy stacks
Safety/ops value	Prevents incidents or reduces operational risk
User-flagged	User says "save this as a skill" or equivalent

Extraction Workflow

1. Identify candidate: learning meets extraction criteria
2. Run helper (or create manually):

   ./skills/self-improving-robotics/scripts/extract-skill.sh skill-name --dry-run
   ./skills/self-improving-robotics/scripts/extract-skill.sh skill-name
   

3. Customize SKILL.md: fill template with telemetry-backed robotics content
4. Update learning: set status to promoted_to_skill, add Skill-Path
5. Verify: ensure extracted skill is clear and safe to apply

Extraction Detection Triggers

In conversation:

• "This keeps happening in field tests"
• "Document this as a reusable robotics workflow"
• "Save this mitigation as a skill"
• "We keep seeing this planner/localization/control failure"

In entries:

• Multiple See Also links
• High/critical resolved issues with strong validations
• Repeated Pattern-Key across environments
• Similar ROB and LRN entries over multiple dates

Multi-Agent Support

Agent	Activation	Detection
Claude Code	Hooks (UserPromptSubmit, PostToolUse)	Automatic via error-detector.sh
Codex CLI	Hooks (same pattern)	Automatic via hook scripts
GitHub Copilot	Manual (.github/copilot-instructions.md)	Manual review
OpenClaw	Workspace injection + inter-agent messaging	Via sessions and shared .learnings/

Stackability Contract (Standalone + Multi-Skill)

This skill is standalone-compatible and stackable with other self-improving skills.

Namespaced Logging (recommended for 2+ skills)

• Namespace for this skill: .learnings/robotics/
• Keep current standalone behavior if you prefer flat files.
• Optional shared index for all skills: .learnings/INDEX.md

Required Metadata

Every new entry must include:

**Skill**: robotics

Hook Arbitration (when 2+ skills are enabled)

• Use one dispatcher hook as the single entrypoint.
• Dispatcher responsibilities: route by matcher, dedupe repeated events, and rate-limit reminders.
• Suggested defaults: dedupe key = event + matcher + file + 5m_window; max 1 reminder per skill every 5 minutes.

Narrow Matcher Scope (robotics)

Only trigger this skill automatically for robotics signals such as:

• planner|localization|slam|trajectory|control loop
• sensor fusion|actuator fault|sim2real|safety stop
• explicit robotics intent in user prompt

Cross-Skill Precedence

When guidance conflicts, apply:

1. security
2. engineering
3. coding
4. ai
5. user-explicit domain skill
6. meta as tie-breaker

Ownership Rules

• This skill writes only to .learnings/robotics/ in stackable mode.
• It may read other skill folders for cross-linking, but should not rewrite their entries.