Advanced Technology
Robotics & Autonomous Systems
Identify kinematic laws, control equations, and environmental interaction models for robotic systems directly from sensor data.
One of 34 industries across 8 sectors served by CDE — the Causal Dynamics Engine for Science and Engineering.
The Challenge
Why Robotics & Autonomous Systems teams still struggle to explain what is happening
Robotic and autonomous systems operate under complex physical dynamics: multi-body kinematics, contact mechanics, sensor-actuator coupling, and environment interaction. These systems generate rich sensor and actuation time-series during development and field operation, encoding the relationships between control inputs, mechanical response, and environmental forces. Despite decades of analytical robotics research, real-world dynamics frequently diverge from textbook models due to friction, compliance, wear, and unmodeled environmental factors that accumulate during deployment in unstructured settings.
First-principles derivations (analytical mechanics, standard kinematic parameterizations, rigid-body assumptions) provide clean frameworks but struggle with real hardware in unstructured environments. Contact dynamics, flexible-body effects, and sensor noise introduce discrepancies that grow with system complexity. System identification can calibrate model parameters but requires pre-specified structures and cannot discover previously unknown dynamics. As systems grow more complex and operate in less controlled settings, the gap between assumed and actual governing dynamics becomes a primary barrier to reliable autonomy.
The CDE Approach
How CDE closes the explanation gap in robotics & autonomous systems
CDE discovers governing dynamics of robotic systems directly from sensor and actuation data. By ingesting time-series from joint encoders, force-torque sensors, inertial measurement units, and vision systems, it identifies the mathematical laws that describe how systems actually behave, including friction, compliance, and coupling effects that analytical models omit. Each discovery is a typed scientific claim with confidence bounds and evidence provenance, giving robotics teams validated dynamics models grounded in empirical hardware behavior.
Physics-informed architectures respect energy conservation and symmetry properties of mechanical systems, producing dynamics models that are physically consistent by construction. Symbolic mode extracts closed-form control laws and interaction models that engineers can inspect and embed into real-time control software. The Evidence Ledger ensures every discovered model includes deterministic replay, enabling teams to reproduce results across hardware revisions.
Discovery Engine
How CDE applies here
Neuro-Symbolic discovery is well suited to robotics, where complex sensor data is processed by the engine's internal methods, but resulting models must be interpretable for control design and safety certification. Structure extraction yields the closed-form equations control engineers require. Causal mode identifies whether performance degradation stems from actuator wear, surface conditions, or control parameter drift. Regime classification detects transitions between distinct operational modes automatically.
Causal Graphs
Discovers directed causal structure from observational data — identifiable causal graphs, regime classifications, and intervention predictions.
Governing Equations
Extracts compact governing laws grounded in the causal structure — interpretable equations your team can read, verify, and compare against known theory.
Intervention Design
Proposes targeted experiments to resolve ambiguous causal edges — maximizing information gain where the causal structure is still uncertain.
Causal Validation
Negative controls, falsification tests, and identifiability analysis applied to every causal claim before promotion to the evidence ledger.
Typed Scientific Claims
What CDE discovers
Every discovery CDE produces is a typed scientific claim — not a black-box prediction, but a governed, reproducible, auditable piece of scientific knowledge with full provenance.
- Kinematic governing equations
- Contact dynamics models
- Control law identification
- Sensor-environment interaction laws
- Multi-body coupling dynamics
Governed Discovery
Make the finding reviewable
Every discovery CDE produces carries the review context around it: a Truth Dial setting, an evidence entry with replay context, and control results including bootstrap stability, out-of-distribution testing, and feature-shuffle validation.
For robotics & autonomous systems, that means teams can compare runs, justify decisions, and decide whether a finding is ready for internal use, external review, or regulated submission.
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Put CDE on a real robotics & autonomous systems problem
Whether you are exploring robotics & autonomous systems data for the first time or scaling an existing research programme, CDE adapts to your workflow. Bring the dataset, the decision pressure, and the constraints. We will map the right discovery path.