A possible thesis thread: robots and robot teams that keep working when some of their parts fail, are wrong, or are compromised. Hacked firmware, a spoofed GPS fix and a sensor that dies while still reporting plausible numbers look the same from the outside, and a robot has to cope with all three.
I come to this from eight years of cyber-physical security at iTrust, where the plant was a water treatment testbed and the defences were detectors and invariants. A robot is a control system that moves, and a robot team is a networked one.
Detection
Every detector I’ve read about depends on something the faulty part doesn’t control: a second sensor, a second network path, the physics of radio, a design document. Hand-written checks cover only what their designer thought of, and learned ones come with no guarantee. More in failure recovery.
Recovery
Most robots stop. Simplex, shields and barrier filters fall back to something conservative, and swarms cut members off. Few papers measure how long a robot stays useful after its safety layer acts. My workshop paper Safe but Stuck is about what stopping costs in the field. See also safe control.
Monitors
A runtime monitor reads the same state estimate as the controller, and none of the ones I’ve read check it. A spoofed sensor fools the safety switch as easily as the controller. See runtime monitoring.
Estimation
Outlier rejection in robust estimation copes well with look-alike places and badly with a coordinated liar. TEASER++ proves exact recovery needs at least three more inliers than adversarial outliers, the same kind of bound as in Byzantine fault tolerance.
Learned components
Robots running the same model make the same mistakes, and VLM judges lean towards calling an attempt a success. See disagreement between models and evaluating language models. Cheap attacks work on them too: a printed sign, a spoken safety phrase, a backdoor that survives fine-tuning (robot security).
Open questions
- How can a team tell a broken teammate from a lying one, using a witness the suspect can’t control? A broken robot can be repaired or given another job; a liar has to be cut off.
- What does recovery look like beyond stopping, and how do you measure it?
- Can a monitor know when its own inputs are uncertified, and say so to its teammates?
- Which alarm guarantees survive drift and an adversary at the same time?
- How should agreement between two robots running the same model be discounted?
Earlier pieces of this on the site: Lying robots (resilient consensus, interactive), the buoy swarm paper, and resilient consensus.
Papers
| Paper | Year | Venue | Domain |
|---|---|---|---|
| Brain2Qwerty v2 | 2026 | arXiv 2026 (Meta AI) | general |
| EmboCoach-Bench | 2026 | arXiv 2026 | land |
| Compression Principle | 2026 | arXiv 2026 (position essay) | general |
| CaRR | 2026 | arXiv 2026 | general |
| Pow3R-SLAM | 2026 | arXiv 2026 | land |
| Discovering antagonists | 2026 | Robotics and Autonomous Systems 2026 | land |
| Semantic DoS | 2026 | arXiv preprint 2026 | general |
| Equivariant graph CBF | 2025 | arXiv 2025 | air |
| DRACo-SLAM2 | 2025 | arXiv preprint | sea |
| Underwater multi-modal spatial perception | 2025 | ICRA 2025 Workshop on Field Robotics | sea |
| RoboTwin 2.0 | 2025 | arXiv 2025 | land |
| AlphaEvolve | 2025 | Google DeepMind white paper, arXiv 2025 | general |
| DeNeR pursuit-evasion | 2025 | IEEE MRS 2025 | air |
| Energy-constrained coverage | 2025 | arXiv 2025 | general |
| UniDepthV2 | 2025 | arXiv 2025 | general |
| Intermittent faults in swarms | 2025 | arXiv 2025 | general |
| AAPD model | 2025 | Royal Society Open Science 2025 | land |
| Confidence-weighted trust consensus | 2025 | arXiv 2025 | land |
| SCAD | 2025 | IEEE S&P 2025 (listed as Oakland 2025 on the authors' site) | general |
| Humanoid security SoK | 2025 | arXiv preprint 2025 | land |
| Range-based integrity monitoring | 2025 | IEEE Robotics and Automation Letters 2025 | air |
| Opti-acoustic semantic SLAM | 2024 | arXiv preprint | sea |
| LLM-as-a-Judge survey | 2024 | arXiv 2024 (v6, October 2025) | general |
| LalaEval | 2024 | COLM 2024 | general |
| ROS RV and field testing guidelines | 2024 | IEEE Transactions on Software Engineering 2024 | general |
| INVARLLM | 2024 | arXiv 2024 (v2, June 2025) | general |
| Self-consistency | 2023 | ICLR 2023 | general |
| Hydra-Multi | 2023 | IROS 2023 | land |
| Role-based MADDPG pursuit | 2023 | ICRA 2023 | air |
| Blockchain token economy | 2023 | Science Robotics 2023 | land |
| Decentralized Blocklist Protocol | 2023 | AAMAS 2023 | general |
| LLM-guided scene graph planning | 2023 | arXiv preprint | land |
| Verified AI | 2022 | Communications of the ACM 65(7) | general |
| BWGNN | 2022 | ICML 2022 | general |
| Resilient task allocation | 2021 | IEEE Robotics and Automation Letters 2021 | land, air |
| SAD DNS resurrections | 2021 | ACM CCS 2021 | general |
| Kimera | 2021 | IJRR 2021 | land |
| Blockchain vs W-MSR | 2020 | Frontiers in Robotics and AI 2020 | land |
| Nav2 | 2020 | IROS 2020 | land |
| ROSMonitoring | 2020 | TAROS 2020 | general |
| SAD DNS | 2020 | ACM CCS 2020 | general |
| Design vs data invariants | 2020 | International Journal of Critical Infrastructure Protection, vol. 28, 2020 | general |
| SOTER | 2019 | DSN 2019 | air |
| Formal methods for robots survey | 2019 | ACM Computing Surveys 52(5) | general |
| RoboChart | 2019 | Software and Systems Modeling 2019 | general |
| Multi-robot assembly survey | 2018 | ACM Computing Surveys 2018 | land |
| Shielded RL | 2018 | AAAI 2018 | general |
| CBF-QP | 2017 | IEEE Transactions on Automatic Control 2017 | land |
| Crazyflie 2.0 platform | 2017 | MMAR 2017 | air |
| Selective classification | 2017 | NeurIPS 2017 | general |
| Calibration | 2017 | ICML 2017 | general |
| Deep ensembles | 2017 | NeurIPS 2017 | general |
| Crazyswarm | 2017 | ICRA 2017 | air |
| MCMAS | 2017 | International Journal on Software Tools for Technology Transfer 19(1) | general |
| Spoof-resilient networks | 2017 | Autonomous Robots 2017 (conference version at RSS 2015) | air, land |
| Safety barrier certificates | 2016 | ACC 2016 | land |
| Observability-aware trajectory optimisation | 2016 | arXiv preprint 2016 | air |
| Distributed attack detection (SSMP) | 2016 | ACM AsiaCCS 2016 | general |
| Shield synthesis | 2015 | TACAS 2015 | general |
| Attack-resilient estimation | 2014 | IROS 2014 | land |
| LTL3 | 2011 | ACM Transactions on Software Engineering and Methodology 20(4) | general |
| STL robustness | 2010 | FORMATS 2010 (LNCS 6246) | general |
| PRISM swarm verification | 2010 | ANTS 2010, Swarm Intelligence (LNCS 6234) | general |
| Brief account of RV | 2009 | Journal of Logic and Algebraic Programming 78(5) | general |
| Z3 | 2008 | TACAS 2008 (LNCS 4963) | general |
| Vicsek model convergence | 2003 | IEEE Transactions on Automatic Control, vol. 48, no. 6, 2003 | general |
| Simplex | 2001 | IEEE Software 2001 | general |
| Hybrid automata | 1996 | LICS 1996 | general |
| Timed automata | 1994 | Theoretical Computer Science 126(2) | general |
| Temporal logic of programs | 1977 | FOCS 1977 | general |
| Paper | Year | Venue | Domain |
|---|---|---|---|
| GS underwater study | 2026 | arXiv 2026 | sea |
| AutoScientists | 2026 | arXiv 2026 | general |
| Reputation-based consensus | 2026 | arXiv preprint | general |
| FRAC-MARL | 2026 | arXiv preprint | general |
| Conformal map fusion | 2026 | arXiv preprint | general |
| CoSMO | 2026 | CoRL 2026 | land |
| MemoGuard | 2026 | IEEE Embedded Systems Letters (ESWEEK CODES 2026) | land |
| CP-Cert | 2026 | arXiv preprint | general |
| RIWM | 2026 | arXiv preprint (perspective) | general |
| FailBench | 2026 | arXiv preprint | general |
| Hide-and-Seek | 2026 | NeurIPS 2026 | general |
| No Free Checker | 2026 | arXiv preprint (survey) | general |
| CILC | 2026 | arXiv preprint | general |
| Trust-aware rollout | 2026 | arXiv preprint | land |
| Detect and Suppress | 2026 | arXiv preprint | general |
| Embodied agent security survey | 2026 | arXiv preprint (survey) | general |
| SABER | 2026 | arXiv preprint | land |
| Multi-agent prompt injection | 2026 | arXiv preprint | general |
| RigidShift | 2026 | arXiv preprint | air |
| Physical prompt injection | 2026 | arXiv preprint | general |
| 3DSG semantic uncertainty | 2026 | arXiv preprint | land |
| Underwater metric depth benchmark | 2025 | arXiv 2025 | sea |
| AQUA-SLAM | 2025 | IEEE T-RO 2025 | sea |
| Agentic Robot | 2025 | arXiv 2025 | land |
| CRISP | 2025 | CVPR 2025 | general |
| SAFE | 2025 | NeurIPS 2025 | general |
| FAIL-Detect | 2025 | RSS 2025 | general |
| BadVLA | 2025 | arXiv preprint | general |
| TURTLMap | 2024 | IROS 2024 | sea |
| π0 | 2024 | RSS 2025 | land |
| ROMAN | 2024 | RSS 2025 | land, air |
| Diffusion Policy | 2023 | RSS 2023 (extended journal version on arXiv) | land |
| ACT | 2023 | arXiv 2023 | land |
| C-3PO | 2023 | IEEE Transactions on Robotics 2023 (first arXiv version 2022, titled "Correct and Certify") | general |
| MDBC | 2021 | ICLR 2021 | land, air |
| TEASER++ | 2021 | IEEE Transactions on Robotics 2021 | general |
| Neural Trees | 2021 | NeurIPS 2021 | general |
| GNC | 2020 | IEEE Robotics and Automation Letters 2020 (ICRA 2020 Best Paper Award in Robot Vision) | general |
| PCM | 2018 | ICRA 2018 | land |
Other threads: multi-robot multimodal perception, long-term autonomy. All three on one page: thesis map.