Expensive models and experiments
PIML can reduce repeated simulation or experimental cost while retaining the governing knowledge used in Automation and Robotics.
Engineering Research Community
Automation and Robotics & Physics-Informed Machine Learning
Physics-grounded modelling, learning and validation for Automation and Robotics
Automation and Robotics integrates sensing, actuation, mechanisms, embedded computation, control, planning and human–machine interaction. It covers industrial robots, mobile robots, drones, collaborative systems, autonomous inspection and flexible production cells.
PIML connects robot data to kinematics, rigid/compliant-body dynamics, contact, actuator limits, conservation and stability. It is most valuable when nominal models are available but friction, payload, flexibility, terrain and contact remain uncertain.
This page presents ten focused research areas, degree-level project pathways, selected publications and direct support through the PIMLS biweekly members meeting.
This Automation and Robotics guide covers Physics-Informed Neural Networks (PINNs), physics-guided machine learning, scientific machine learning, neural operators, hybrid models and engineering digital twins. Explore the research and project pathways below, then join the Physics-Informed Machine Learning Society to connect with the international PIMLS community.
The central ideaEstablished Automation and Robotics knowledge + measurements and simulation + machine learning
10focused research areas
3academic project pathways
6selected publications
Biweeklymember research meeting
Why this combination matters
Why Automation and Robotics Needs Physics-Informed Learning
Use available scientific knowledge to make limited data more useful, transparent and testable.
Incomplete engineering models
Learn uncertain parameters, closures or discrepancies around an inspectable mechanistic foundation.
Transfer across conditions
Test whether structured models generalize across geometries, materials, assets, operating regimes or sites.
Trustworthy evidence
Use physical residuals, independent measurements, uncertainty and conventional engineering baselines before deployment.
Ten focused directions
Major Automation and Robotics PIML Research Areas
Each card connects a meaningful Automation and Robotics question with suitable scientific knowledge, modelling choices and evidence needed to test it.
01
Manipulator Inverse Dynamics
Joint torque depends on inertia, Coriolis, gravity, friction and payload. PIML opportunities: Embed robot equations and learn uncertain friction/compliance residuals.
Model and evidenceGoverning equations, calibrated measurements and held-out operating conditions
02
Compliant and Soft Robots
Elastic components improve safety but create distributed nonlinear dynamics. PIML opportunities: Combine energy-based models with learned constitutive or damping terms.
Model and evidenceMechanistic and data-only baselines, uncertainty and independent validation
03
Mobile-Robot Dynamics
Wheel slip and terrain change motion response. PIML opportunities: Retain nonholonomic dynamics and learn terrain-dependent slip with uncertainty.
Model and evidenceGeometry, material or system parameters, sensor data and physical residuals
04
Aerial Robotics
Aerodynamics, ground effect and payload disturb nominal flight models. PIML opportunities: Use momentum/dynamics constraints for identification, prediction and robust control.
Model and evidenceGoverning equations, calibrated measurements and held-out operating conditions
05
Contact-Rich Manipulation
Impact, friction and intermittent contact challenge smooth models. PIML opportunities: Use hybrid/contact modes and learned residuals with independent force validation.
Model and evidenceMechanistic and data-only baselines, uncertainty and independent validation
06
Human–Robot Collaboration
Variable human forces and intent affect safe motion. PIML opportunities: Fuse robot dynamics, impedance/passivity constraints and human-state uncertainty.
Model and evidenceGeometry, material or system parameters, sensor data and physical residuals
07
Robot Calibration
Kinematic, inertial and sensor parameters drift or are uncertain. PIML opportunities: Solve physics-informed inverse problems with excitation and identifiability analysis.
Model and evidenceGoverning equations, calibrated measurements and held-out operating conditions
08
Digital Twins of Robot Cells
Cycle-time, wear, collision and quality must be predicted online. PIML opportunities: Update a mechanistic cell model from synchronized telemetry and inspection data.
Model and evidenceMechanistic and data-only baselines, uncertainty and independent validation
09
Predictive Maintenance
Rare failures appear through motor current, vibration and tracking residuals. PIML opportunities: Use degradation physics and dynamics-derived features for fault/RUL estimation.
Model and evidenceGeometry, material or system parameters, sensor data and physical residuals
10
Vision–Dynamics Fusion
Visual estimates may be geometrically plausible but dynamically impossible. PIML opportunities: Constrain learned state and motion using geometry, kinematics and temporal dynamics.
Model and evidenceGoverning equations, calibrated measurements and held-out operating conditions
Choose the right research depth
Projects for Every Academic Stage
Start with a scope that matches your time, mathematical background, experimental access and expected research contribution.
Project pathway 1
B.E./B.Tech
Learn the foundations with a bounded, measurable system.
- Lagrangian manipulator residual model
- wheel-slip-aware mobile robot estimator
- quadrotor hybrid dynamics benchmark
- physics-guided motor fault detector
Expected outcome
A reproducible implementation, clear baselines, a manageable dataset and physically meaningful validation.
Project pathway 2
M.Tech
Combine an engineering model, substantial data and rigorous comparison.
- compliant-robot neural dynamics
- contact-mode hybrid model
- safe hybrid MPC testbed
- robot-cell digital twin
Expected outcome
A thesis-quality study with held-out regimes, mechanistic and data-only baselines, ablation and uncertainty.
Project pathway 3
Ph.D.
Address a publishable methodological, multiscale or deployment research gap.
- certifiable physics-informed robot learning
- multi-contact differentiable autonomy
- human–robot hybrid dynamics with uncertainty
- transferable foundation models for robot physics
Expected outcome
New methodology or validated engineering insight, multi-regime evidence, reproducible software and journal publications.
From idea to evidence
A Strong PIML Project Workflow
01
Define
Choose one Automation and Robotics question and a measurable engineering output.
02
Model
State the governing relationships, constraints or validated domain knowledge you will retain.
03
Compare
Build mechanistic and data-only baselines before the hybrid model.
04
Validate
Hold out experiments, conditions, assets, sites or regimes at the deployment level.
05
Publish
Report uncertainty, ablation, limitations, data lineage and reproducible code.
Read before you modelDiscuss Your Literature
Selected Publications and Why They Matter
Use this focused reading list to understand the general PIML framework, direct Automation and Robotics evidence and suitable hybrid modelling methods.
Literature review advice
Do not list papers only. Compare the engineering question, incorporated knowledge, data, split strategy, baselines, uncertainty and evidence level.
This source is included in the Automation and Robotics literature guide because it demonstrates or reviews a relevant physics-informed, hybrid, inverse, surrogate or scientific-machine-learning approach. Read the methods, data split, baselines and validation evidence—not only the reported accuracy.
How to use this paper: Use this paper to refine the research question, identify a defensible physical prior and compare evidence requirements for Automation and Robotics.
Read publication or record This source is included in the Automation and Robotics literature guide because it demonstrates or reviews a relevant physics-informed, hybrid, inverse, surrogate or scientific-machine-learning approach. Read the methods, data split, baselines and validation evidence—not only the reported accuracy.
How to use this paper: Use this paper to refine the research question, identify a defensible physical prior and compare evidence requirements for Automation and Robotics.
Read publication or record This source is included in the Automation and Robotics literature guide because it demonstrates or reviews a relevant physics-informed, hybrid, inverse, surrogate or scientific-machine-learning approach. Read the methods, data split, baselines and validation evidence—not only the reported accuracy.
How to use this paper: Use this paper to refine the research question, identify a defensible physical prior and compare evidence requirements for Automation and Robotics.
Read publication or record This source is included in the Automation and Robotics literature guide because it demonstrates or reviews a relevant physics-informed, hybrid, inverse, surrogate or scientific-machine-learning approach. Read the methods, data split, baselines and validation evidence—not only the reported accuracy.
How to use this paper: Use this paper to refine the research question, identify a defensible physical prior and compare evidence requirements for Automation and Robotics.
Read publication or record This source is included in the Automation and Robotics literature guide because it demonstrates or reviews a relevant physics-informed, hybrid, inverse, surrogate or scientific-machine-learning approach. Read the methods, data split, baselines and validation evidence—not only the reported accuracy.
How to use this paper: Use this paper to refine the research question, identify a defensible physical prior and compare evidence requirements for Automation and Robotics.
Read publication or record This source is included in the Automation and Robotics literature guide because it demonstrates or reviews a relevant physics-informed, hybrid, inverse, surrogate or scientific-machine-learning approach. Read the methods, data split, baselines and validation evidence—not only the reported accuracy.
How to use this paper: Use this paper to refine the research question, identify a defensible physical prior and compare evidence requirements for Automation and Robotics.
Read publication or record Build an interdisciplinary team
Where Automation and Robotics Can Collaborate
Computer Science
Scientific ML, optimization, trustworthy AI and reproducible research software.
Applied Mathematics
Differential equations, numerical methods, inverse problems and uncertainty.
Sensing & Control
Instrumentation, data acquisition, state estimation and responsible deployment.
Domain Laboratories
Experiments, calibration, validation evidence and practical expertise for Automation and Robotics.
Before you begin
Frequently Asked Research Questions
These answers help students avoid common scope, terminology and validation mistakes.
Still have a question?
Use the biweekly meeting form for research guidance.
Request accessNo. Automation and Robotics projects may use physics-guided features, hybrid residual models, differentiable simulators, neural operators, constrained architectures or data assimilation. State exactly what knowledge is incorporated.
Choose one engineering question, a measurable output and a defensible mechanistic baseline. Add learning only where data can identify an uncertainty or discrepancy.
A meaningful question, justified prior knowledge, deployment-level holdouts, strong baselines, ablation, uncertainty, reproducibility and honest limitations.
Simulation can broaden coverage, but simulation-only evidence cannot establish real-system accuracy. Use calibrated experiments, field measurements or trusted independent references appropriate to the claim.
Submit the biweekly members meeting form to discuss your project level, branch, data, model, validation plan and possible collaborators.
Take the next step
Bring your Automation and Robotics research idea to PIMLS
Join the biweekly members meeting for project guidance, collaboration and publication planning—or contact the Society directly.
Meeting participation is requested through the Google form. Complete it carefully so the Society can understand your research interest.