Explainable AI for Robotics: Trust You Can Operate

A mobile robot that can’t explain itself is a liability. Not because it’s “mysterious,” but because the moment something goes wrong—an unexpected stop, a detour around a pallet, a near-miss with a human—operators need answers they can act on.

That’s why explainable AI for robotics is moving from “nice to have” to operational requirement across logistics, manufacturing, and healthcare. When robots share spaces with people, silence is expensive: it slows incident response, complicates compliance, and erodes adoption.

Amar Halilovic, a PhD researcher at Ulm University, is working on a practical version of explainability: robots that generate context-sensitive explanations of navigation decisions, especially in failure cases. His focus is Human-Robot Interaction (HRI), but the implications are straight industrial automation: fewer stalled deployments, faster troubleshooting, and higher trust on the floor.

Explainable AI in robotics isn’t a “UI feature”—it’s safety and uptime

Explainability is part of the control loop in real deployments. If you’re running autonomous mobile robots (AMRs) in a warehouse or mobile manipulators in a hospital, you’re not just managing routes—you’re managing humans’ mental models of what the robot will do next.

Here’s the stance I’ve seen play out: most teams treat explanations as something you add after the navigation model is “done.” That approach fails in production because the hardest moments aren’t the normal ones. The hardest moments are:

• The robot stops in a “clear” hallway (but its sensors disagree)
• The robot reroutes around a zone the map says is open
• The robot slows down near people in a way that looks indecisive
• The robot refuses a task because it predicts congestion or risk

In those moments, operators and nearby workers ask a simple thing: “What did you see, and why did you choose that?”

A usable explanation reduces downtime in three ways:

1. Faster triage: Is it a perception issue, a map issue, a policy constraint, or a safety layer trigger?
2. Better recovery actions: “Move object X” beats “reboot robot.”
3. Lower escalation: Clear explanations prevent every hiccup from becoming a ticket to the robotics team.

This is the core connection to our AI in Robotics & Automation series: transparent automation scales better than opaque automation because it distributes understanding across the operation.

What “good robot explanations” look like in navigation work

Good explanations are aligned with human expectations and the situation, not the model’s internal math. Halilovic’s research emphasizes generating explanations that match what people find helpful—particularly during navigation failures.

That’s a subtle but critical point: you can expose attention maps, probabilities, or raw sensor overlays—and still fail to explain anything to the person who needs to make a decision in 10 seconds.

Environmental explanations: start with the world, not the algorithm

One practical approach highlighted in Halilovic’s work is environmental explanations—explanations grounded in what’s happening around the robot.

Examples of environmental explanation statements that operators can actually use:

• “Path blocked by obstacle in aisle 3; waiting for clearance.”
• “No safe clearance to pass the pedestrian; slowing down.”
• “Localization confidence dropped; returning to last known marker.”
• “Doorway appears open in map but is closed; rerouting.”

Notice what these do: they translate a technical condition into an actionable world-state.

Black-box and generative explanations: different strengths, different risks

Halilovic explores both black-box and generative approaches for producing explanations in text and visuals.

• Black-box explanations (post-hoc) can summarize why a decision happened without changing the underlying navigation stack. This is attractive for teams retrofitting explainability onto existing AMR fleets.
• Generative explanations can produce richer, more natural descriptions and visual narratives (“I chose the left corridor because the right corridor has moving obstacles”). But they also raise a deployment risk: a fluent explanation that’s wrong is worse than no explanation.

My opinion: in industrial automation, fidelity beats fluency. Start with constrained, verifiable explanation templates tied to measurable triggers (costmap obstacle, safety stop reason, localization uncertainty), then expand to generative language only where you can validate it.

Timing and format matter more than most teams think

The same explanation can feel helpful or insulting depending on timing. Halilovic notes that people interpret robot behavior differently depending on urgency and failure context—and that expectation changes what “good” explainability is.

This shows up in real facilities:

• During normal operation, workers prefer minimal interruptions.
• During a failure or near-miss, they want detail immediately.

Explanation attributes you should design—explicitly

Halilovic has worked on planning explanation attributes such as timing, representation, and duration. Translating that into deployment terms, you should decide—up front—how your robots handle:

• When to explain: proactively (before action), reactively (after event), or on-demand (when asked)
• How much to explain: short status vs. multi-step reasoning
• Where to explain: on-robot screen, tablet UI, control room dashboard, voice, stack light patterns
• How long to persist: transient toast vs. logged incident report

A practical pattern I recommend:

1. Default: short, low-noise status (“Slowing for pedestrian”)
2. Escalation: richer details after abnormal triggers (“Safety stop: obstacle detected within 0.6m; confidence 0.92”)
3. Forensics: full logs with sensor snapshots and policy state for engineering review

If you’re selling or deploying robotics solutions, this is a lead-generation truth: customers don’t just buy navigation accuracy—they buy operational clarity.

Dynamic, context-aware explainability is where deployments get easier

Static explanations don’t work across roles. A shift supervisor, a technician, and a nurse need different information. Halilovic’s recent direction—dynamically selecting the best explanation strategy depending on context and user preferences—maps directly onto how robots are adopted in mixed environments.

Personalization isn’t optional in human-robot interaction

In a warehouse, a picker might only need “Wait” or “Go.” A robotics technician might need: “I’m waiting because the safety layer flagged a dynamic obstacle with uncertain velocity.”

Treat “user preference” as a real system input:

• Role-based defaults (operator vs. engineer)
• Experience level (new staff vs. expert)
• Safety posture (high-risk zones vs. open areas)
• Urgency (routine task vs. incident)

Real-time adaptation: learning from feedback

Halilovic plans to extend the framework toward real-time adaptation, where robots learn from user feedback and adjust explanations on the fly.

That’s exactly the direction the industry needs. A good explanation system should improve like any other part of the product: through feedback, evaluation, and iteration.

A simple, high-impact workflow:

• Robot provides an explanation
• UI offers quick feedback: “Helpful / Not helpful” + optional reason
• System learns which explanation style works per zone, shift, and role

It’s not glamorous, but it’s how you reduce repeated confusion—the kind that slowly kills automation ROI.

Industrial examples: where explainable robot navigation pays off

Explainable AI turns navigation from “robot magic” into a manageable process. Here are concrete ways it lands in core automation sectors.

Logistics: less downtime, fewer escalations

AMR fleets fail in boring ways: reflective wrap on pallets, seasonal layout changes, temporary staging areas during peak shipping, human traffic spikes.

Explainability helps by:

• Pointing to the specific condition (blocked aisle vs. low localization confidence)
• Reducing unnecessary resets (“Clear obstacle and retry”)
• Improving shift handoffs (logged reasons for delays)

In December operations—when throughput pressure is high and layouts are constantly changing—this matters even more. Peak season is where brittle autonomy gets exposed.

Manufacturing: safer shared spaces

In manufacturing cells, the cost of misunderstanding is higher. Robots interacting with humans need to communicate intent clearly.

Explainable navigation supports:

• Clear intent during slowdowns and detours
• Fewer “human overrides” caused by uncertainty
• Better incident documentation for safety reviews

Healthcare: trust is the product

Hospitals don’t tolerate “because the model said so.” They require predictable behavior, especially in hallways and patient areas.

Explainable robot navigation can:

• Reduce staff frustration (“Waiting for elevator crowd to clear”)
• Increase acceptance for service robots (deliveries, transport)
• Support governance when autonomy policies are questioned

My view: healthcare will be one of the strictest filters for explainability because the users are busy, the stakes are high, and patience is low.

How to evaluate explainable AI for robotics (a buyer’s checklist)

If you’re considering robots with explainable AI—or you’re building them—evaluate explanations like you evaluate uptime. Here’s a checklist that’s practical for pilot projects.

Questions to ask vendors or internal teams

1. Can the robot explain failures in plain language? Ask for examples tied to real triggers.
2. Are explanations consistent with logs? If the UI says “Obstacle,” can you verify it in the costmap/sensor snapshot?
3. Do explanations adapt by role and urgency? Operators need brevity; engineers need traceability.
4. Is there an “explain on demand” function? One-tap “Why did you stop?” matters.
5. Are explanations measurable? You should be able to A/B test formats against metrics like mean time to recovery.

Metrics that actually matter

• Mean time to recovery (MTTR) after a stop
• Number of manual interventions per shift
• Escalation rate (incidents that require robotics engineers)
• Operator confidence scores from lightweight surveys
• Repeat-incident frequency in the same location

Explainability earns its keep when these numbers move.

Where this research is headed—and what to do next

Explainable AI for robotics is becoming the interface between autonomy and operations. Halilovic’s focus on context-sensitive explanations, timing, and real-time adaptation points to a future where robots don’t just navigate—they communicate intent and constraints the way good coworkers do.

If you’re deploying AI-driven automation, treat explainability as part of the system architecture. Build it into incident workflows. Train staff on it. Measure it. The payoff isn’t abstract trust—it’s fewer stalled robots, smoother human-robot interaction, and faster scaling across sites.

As this AI in Robotics & Automation series continues, a question worth sitting with is simple: when your robots make a “reasonable” decision that humans misinterpret, who carries the cost—and how quickly can your system explain its way out of trouble?