Stanford Robotics Seminar ENGR319 | Spring 2026 | Integrated Learning and Planning
TL;DR
This seminar presents a neuro-symbolic approach to robot learning that combines neural visual representations with physics-based constraint optimization to enable one-shot skill acquisition, achieving over 90% success rates on novel objects compared to 0% for standard policy learning methods.
⚠️ The Data Efficiency Problem in Modern Robotics 3 insights
Current approaches require massive datasets
End-to-end policy learning systems like those from Physical Intelligence need hundreds of hours of teleoperation data just to learn simple tasks like folding boxes or basic actuator manipulation.
Pure policy learning lacks compositionality
Standard approaches cannot stitch together separate skills (like picking up tools) to solve novel multi-step tasks because they fail to account for how early actions constrain later ones.
Human-level generalization remains unmatched
Humans can learn complex manipulation tasks from a single demonstration and generalize to new objects, states, and goals instantly, while current robotic systems fail to transfer across categories.
🧠 Neuro-Symbolic Planning Framework 3 insights
World models plus planning over policies
The proposed paradigm treats physical intelligence as compositional world modeling—representing objects, properties, and actions abstractly—combined with search-based planning rather than direct policy execution.
Actions as constraint optimization
Instead of learning fixed policies, the system models actions as constraint satisfaction problems that combine rigid body physics, geometric constraints, and task-specific requirements, enabling temporal and spatial composition.
Two-level learning architecture
The framework learns which constraints are task-relevant from demonstrations, then uses physics simulators and motion planners to generate trajectories that satisfy those constraints without learning physics from scratch.
🎯 One-Shot Learning Results 3 insights
Visual correspondence with DINOv2
The system uses pretrained visual features to identify functional contact points on novel objects from a single demonstration, then verifies proposals through physics-based stability analysis and motion planning.
90% success on out-of-distribution objects
On a hanging task benchmark with completely unseen 3D-printed alphabetical shapes, the method achieved over 90% success compared to 0% for pure policy learning approaches and marginal gains for neural feature-only methods.
Cross-category generalization
The system generalizes from demonstrations on hangers to successfully manipulate mugs, kitchenware, and arbitrary geometric shapes without additional training data by reasoning about functional correspondence.
Bottom Line
Robotics should abandon data-heavy end-to-end policy learning in favor of neuro-symbolic systems that combine neural perception with physics-based constraint optimization, enabling robots to learn complex skills from single demonstrations and reliably generalize to novel objects.
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